Dynamic network security shielding system

Abstract

Methods and systems for dynamic shielding in network security is disclosed. The method comprises analyzing real-time network traffic to detect a potential threat and assessing a severity level for each detected threat. Vulnerabilities of the devices in the network and the communications network to which each device is connected can be determined. A risk score can be generated for the threat based on the severity level of the threat and the vulnerabilities in the network. A response strategy can be implanted based on the risk score. The response strategy can include predefined actions for risk scores below a threshold, and maximum protective measures for risk scores above the threshold. The methods and systems can employ machine learning algorithms for threat detection and classification and utilize adaptive learning to refine future response strategies based on historical data of network interactions and system responses.

Description

DYNAMIC NETWORK SECURITY SHIELDING SYSTEMCROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] The present application claims priority to United States Provisional Patent Application No. 63 / 602,099, titled “DYNAMIC SHIELDING SYSTEM FOR NETWORK SECURITY,” and filed November 22, 2023, the entirety of which is incorporated by reference hereto.TECHNICAL FIELD

[0002] The present disclosure relates generally to network security systems and methods for protecting digital infrastructures. More particularly, the present disclosure relates to a dynamic shielding system that adaptively responds to security threats based on a real-time assessment of attack severity, device vulnerability, and network vulnerability.BACKGROUND

[0003] Traditional network security systems play a fundamental role in protecting digital infrastructures, but they face several limitations in today's rapidly evolving threat landscape. These systems typically rely on static rules and configurations, which may not adapt quickly enough to new and emerging threats. This lack of adaptability can potentially compromise the security of a computer network and the devices connected to these networks.

[0004] A significant shortcoming of many security systems is their limited capability for realtime (or near real-time) communication between different endpoints or devices protected by security agents. This communication can be useful to provide rapid threat identification, coordinated response to compromised devices, and network-wide isolation of threats to the network.

[0005] When a threat is identified in one part of a network, such as malware impacting a computing node in the network, immediate communication to other parts of the network can be essential to mitigate the threat. However, some systems often fail to disseminate thisinformation promptly across the network, leaving other areas vulnerable to the same threat. Similarly, in situations where a device becomes compromised, it poses a risk to other devices on the network. Without efficient mechanisms to alert other devices about the compromised device, responses may be delayed or inadequate.

[0006] Effective isolation of a breached device can be vital to prevent the spread of threats. However, without real-time communication capabilities, many systems may struggle to execute timely isolation, potentially allowing threats to proliferate within the network. Additionally, the ability to share threat intelligence among devices in real-time allows for an adaptive, network-wide defense strategy. Traditional systems often operate in silos, missing the opportunity to leverage collective intelligence for enhanced security.

[0007] Furthermore, many security solutions lack the ability to dynamically assess and respond to threats based on the specific vulnerabilities of individual devices and the overall network. This one-size-fits-all approach may result in either over-reactive responses that unnecessarily disrupt network operations or under-reactive measures that fail to adequately protect against serious threats.

[0008] As cyber threats continue to grow in sophistication and frequency, there is an increasing need for more dynamic and intelligent security systems. Such systems should be capable of not only detecting and responding to threats in real-time but also effectively communicating threat information across various endpoints. A more adaptive approach to network security can ensure a more robust defense against both internal and external cyber threats, leveraging the collective security intelligence of all connected devices.SUMMARY

[0009] The present embodiments relate to methods and systems for dynamic shielding in network security. The network traffic can be processed to detect a potential threat and assessing a severity level for each detected threat. Vulnerabilities of the devices in the network and thecommunications network to which each device is connected can be determined. A risk score can be generated for the threat based on the severity level of the threat and the vulnerabilities in the network. A response strategy can be implanted based on the risk score. The response strategy can include predefined actions for risk scores below a threshold, and maximum protective measures for risk scores above the threshold. The methods and systems can employ machine learning algorithms for threat detection and classification and utilize adaptive learning to refine future response strategies based on historical data of network interactions and system responses.

[0010] In a first example embodiment, a computer-implemented method performed by a dynamic network security shielding system is provided. The computer-implemented method can include receiving a set of network traffic in a computer network that comprises a set of computing nodes. The computer-implemented method can also include analyzing the network traffic to detect a threat and determining a severity level for each detected threat.

[0011] The computer-implemented method can also include evaluating a vulnerability of at least a first computing node in the set of computing nodes and a communications network that the first computing node is connected. The computer-implemented method can also include generating a risk score that is based on a combination of the determined severity level of the threat and the evaluated vulnerability of at least the first computing node and the communications network.

[0012] Responsive to the generated risk score exceeding a threshold, the computer- implemented method can also include implementing a maximum number of security actions to the set of computing nodes and the communications network. Responsive to the generated risk score not exceeding a threshold, the computer-implemented method can also include implementing a limited number of predefined actions specific to the first computing node and / or the communications network.

[0013] In some instances, analyzing the network traffic is performed by a machine learning model configured to identify one or more patterns in the network traffic and identify anomalies from the identified patterns that are indicative of the detected threat.

[0014] In some instances, evaluating the vulnerability of at least the first computing node and the communications network comprises analyzing a software version of the first computing node, identify a software patch level of the first computing node, identifying a historical security incidents relating to the first computing node or the communications network, identifying a topology of the communications network, and identifying one or more known vulnerabilities of the first computing node and / or the communications network.

[0015] In some instances, the limited number of predefined actions include at least one of: isolating one or more network segments of the first computing node, blocking one or more network ports associated with a subset of internet protocol (IP) addresses, and generating an alerts for manual intervention that specifies the alert and the evaluated vulnerability of the first computing node.

[0016] In some instances, the computer-implemented method further includes storing a set historical data relating to network interactions and system responses in a database.

[0017] In some instances, the computer-implemented method further includes detecting a second threat in the network traffic, processing the stored historical data to identify a result of implementing the maximum number of security actions or the limited number of predefined actions, and refining a risk score generated for the second threat based on the identified result of implementing the maximum number of security actions or the limited number of predefined actions in response to the threat.

[0018] In some instances, a set of threat detection parameter and response thresholds are generated from the identified result of implementing the maximum number of security actions or the limited number of predefined actions in response to the threat.

[0019] In another example embodiment, a network security system is provided. The network security system can include a threat assessment engine configured to analyze a set of network traffic to detect a threat and determine a severity level of the detected threat. The network security system can also include a vulnerability analysis module configured to estimate a vulnerability of at least a first computing node and a communications network.

[0020] The network security system can also include a risk scoring subsystem configured to generate a risk score for the threat based on the determined severity level of the threat and the vulnerability of at least the first computing node and the communications network. The network security system can also include a response strategy system configured to activate one or more security measures based on the cumulative risk score.

[0021] In some instances, the threat assessment engine is configured to implement a machine learning model to detect the threat from the set of network traffic.

[0022] In some instances, the estimated vulnerability of the first computing node and the communications network is based on any of a software version of the first computing node, a patch level of the first computing node, a set of historical security incidents of the first computing node or the communications network, and a topology of the communications network.

[0023] In some instances, the response strategy system is configured to implement a maximum set of protective measures when the generated risk score exceeds a predefined threshold.

[0024] In some instances, the maximum set of protective measures include at least one of: a complete network isolation of at least the first computing node, comprehensive port blocking of at least the first computing node, and immediate deployment of security patches to at least the first computing node.

[0025] In some instances, the network security system further includes a database configured to store historical data on network interactions and system responses.

[0026] In some instances, the response strategy system is further configured to utilize the stored historical data to refine future response strategies through adaptive learning.

[0027] In another example embodiment, a non-transitory computer-readable medium is provided. The non-transitory computer-readable medium can store instructions that, when executed by a processor, cause the processor to perform a method for adapting network security responses. The method can include receiving a set of network traffic in a computer network that comprises a set of computing nodes.

[0028] The method can also include analyzing the network traffic to detect a threat and determining a severity level for each detected threat. The method can also include evaluating a vulnerability of at least a first computing node in the set of computing nodes and a communications network that the first computing node is connected. The method can also include generating a risk score that is based on a combination of the determined severity level of the threat and the evaluated vulnerability of at least the first computing node and the communications network.

[0029] The method can also include, responsive to the generated risk score exceeding a threshold, implementing a maximum number of security actions to the set of computing nodes and the communications network. The method can also include, responsive to the generated risk score not exceeding a threshold, implementing a limited number of predefined actions specific to the first computing node and / or the communications network.

[0030] In some instances, analyzing the network traffic is performed by a machine learning model configured to identify one or more patterns in the network traffic and identify anomalies from the identified patterns that are indicative of the detected threat.

[0031] In some instances, evaluating the vulnerability of at least the first computing node and the communications network comprises analyzing a software version of the first computing node, identify a software patch level of the first computing node, identifying a historicalsecurity incidents relating to the first computing node or the communications network, identifying a topology of the communications network, and identifying one or more known vulnerabilities of the first computing node and / or the communications network.

[0032] In some instances, the limited number of predefined actions include at least one of: isolating one or more network segments of the first computing node, blocking one or more network ports associated with a subset of internet protocol (IP) addresses, and generating an alerts for manual intervention that specifies the alert and the evaluated vulnerability of the first computing node.

[0033] In some instances, the instructions further cause the processor to perform the method comprising storing a set historical data relating to network interactions and system responses in a database.

[0034] In some instances, the instructions further cause the processor to perform the method comprising detecting a second threat in the network traffic, processing the stored historical data to identify a result of implementing the maximum number of security actions or the limited number of predefined actions, and refining a risk score generated for the second threat based on the identified result of implementing the maximum number of security actions or the limited number of predefined actions in response to the threat.BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and form a part of the specification, illustrate the embodiments of the invention and together with the written description serve to explain the principles, characteristics, and features of the invention. Various aspects of at least one example are discussed below with reference to the accompanying drawings, which are not intended to be drawn to scale. In the drawings:

[0036] FIG. 1 illustrates an example network including a dynamic network security shielding system in accordance with an embodiment.

[0037] FIG. 2 depicts an illustrative flowchart for an exemplary dynamic shielding method in network security in accordance with an embodiment.

[0038] FIG. 3 shows an example computing device which may be used in the systems and methods described herein.DETAILED DESCRIPTION

[0039] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a sufficient understanding of the subject matter presented herein. But it will be apparent to one of ordinary skill in the art that the subject matter may be practiced without these specific details. Moreover, the particular embodiments described herein are provided by way of example and should not be used to limit the scope of the invention to these particular embodiments. In other instances, well- known data structures, timing protocols, software operations, procedures, and components have not been described in detail so as not to unnecessarily obscure aspects of the embodiments of the invention.Overview

[0040] The present embodiments relate to a dynamic network security shielding system implementing an advanced approach to network security, designed to address the evolving challenges in protecting digital infrastructures. The system as described herein can implement multiple components working in concert to provide comprehensive and adaptive security measures.

[0041] The dynamic network security shielding system can include any of a threat assessment engine, a vulnerability analysis module, a risk scoring mechanism, and a responsestrategy system. These components work together to continuously monitor network traffic, identify potential threats, assess vulnerabilities, and implement appropriate security measures.

[0042] The system can implement a real-time (or near real-time) analysis of network traffic to detect and classify potential security threats. The system can implement various techniques such as machine learning models, neural networks, etc., to identify patterns in the network traffic and detect anomalies in the network traffic. For instance, a machine learning algorithm can be implemented to analyze network traffic to detect a sharp increase in network traffic from known malicious internet protocol (IP) addresses or a threshold number of requests reaching a specific server that can be indicative of a distributed denial of service (DDOS) attack.

[0043] The system can concurrently evaluate the vulnerability of individual devices and the network itself, considering factors such as software versions, patch levels, and historical security incidents. For instance, the system can determine that a specific device in a network has an increased vulnerability due to a device type (e.g., an internet of things (loT) device) or a device having out of date software patches or operating system versions that can be indicative of an increased vulnerability to malware.

[0044] The threat assessment can be combined with the vulnerability analysis results to calculate a comprehensive risk score for the network. This score can serve as the basis for determining a response strategy. For example, in cases where the risk score exceeds a predefined threshold, the system may activate a maximum number of protective measures. The maximum number of protective measures can prioritize device security over network performance, such as by analyzing inbound network packets or preventing specific device functionalities of devices deemed to be vulnerable to malware. While potentially reducing network performance metrics, the maximum number of protective measures prioritize device security to mitigate security threats. For lower risk scenarios, the system may implement predefined actions tailored to the specific type of threat detected. For example, if the risk ismalware, the lower risk scenario may result in selectively implementing limited functionality for only loT devices in the network or devices that have out of date software versions.

[0045] The dynamic network security shielding system can adapt and obtain feedback from past incidents to be used in future network security incidents. For example, if a first incident relates to a DDOS attack to a server and limiting requests from a region mitigates the attack, the results can be tracked and relied upon in a second incident involving similar circumstances. The system can store historical data on network interactions and system responses, which may be used to refine future response strategies through adaptive learning processes.

[0046] In some cases, the dynamic network security shielding system may be designed to integrate with existing network infrastructures and security systems. This integration capability can enhance a network’s current security posture without a complete overhaul of the existing network architecture. For example, the system as described herein can be implemented on one or more computing devices that can connect to an existing security system. The system may complement and augment existing security measures, providing an additional layer of dynamic protection against evolving cyber threats.

[0047] The scalability of the dynamic network security shielding system can allow for deployment across networks of varying sizes and complexities. This flexibility can make the system suitable for a wide range of applications, from small enterprise networks to large- scale corporate environments.

[0048] By providing a comprehensive, adaptive, and integrative approach to network security, the dynamic network security shielding system can address many of the limitations associated with various static security measures, offering a more robust defense against the complex and ever-changing landscape of cyber threats.

[0049] The systems and methods as described herein can provide threat detection. This can include identifying that a device is compromised. The detection can include details about thethreat type (e.g., malware, botnet activity, insider breach). Further, all agents running or in communication with the devices in the network can obtain a notification of the threat. The system can broadcast a threat alert to all devices with installed agents in the network. The alert can specify the compromised device and a nature of the threat. The threat can be assessed locally at the device-level for each device in the network. The assessment can evaluate the potential impact of the threat on the compromised device and the network. Further, a vulnerability check can assess the vulnerabilities of the device (e.g., unpatched software, weak configurations) that might make it susceptible to the identified threat.

[0050] In some cases, a rulebook or playbook can be used based on the threat type. Each agent can refer to a predefined rulebook or playbook that is tailored to the detected threat type. The playbook can specify protection measures required to shield the device and response actions based on severity and vulnerabilities (e.g., block communication, log events).

[0051] A self-shielding response can include, based on the rulebook, the agents implementing protective measures, such as preventing all incoming / outgoing connections with the impacted device, adjusting firewall rules, enabling stricter access controls, or triggering local threat scans to ensure no spread of the threat to other devices. The agents can continue monitoring their respective devices for any signs of compromise or further interaction attempts from the impacted device.

[0052] In some cases, the system notifies administrators of the actions taken and provides logs for the threat's spread potential and / or defensive measures executed by each agent.

[0053] In some instances, if malware is detected, the devices locally can access the severity of the malware (e.g., as self-replicating) and determine a vulnerability of each device (e.g., missing malware signature or unpatched software). The rulebook can be used to take appropriate action, and the logs can record the threat and the actions taken.

[0054] The system as described herein can implement an endpoint detection and response (EDR) architecture that can continuously monitor endpoints for evidence of threats and performs automatic actions to help mitigate them. The system can accumulate multiple indicators of compromise in a time window and then sample general indicators and in a time window, defined as an attack, and raise a level as an indicator of compromise. By filtering events and performing pattern recognition at multiple layers, attacks can be detected.

[0055] Threats can be detected both locally and also centrally across the network. For instance, if a threat is identified locally, the alert can be communicated through an EDR port with others to broadcast the alert. Each device can locally identify a threat, and adjacent devices can locally determine a vulnerability or severity of the threat. Scores can be generated for each device locally and at the system overall, with scores weighted based on a device type and the threat type.

[0056] The agents can run or communicate with each device locally to protect each device and also communicate information to other devices in the network. The agents can perform, in combination with the detection system, can perform the processing and scoring processes as described herein.Network Examples

[0057] FIG. 1 illustrates an example network 100 including a dynamic network security shielding system 102. As shown in FIG. 1, the system can include dynamic network security shielding system 102 electrically connected to different devices in the network, such as computing node 118, servers 120, and user device 122.

[0058] The dynamic network security shielding system 102 can include a threat assessment engine 104. The threat assessment engine 104 may be configured to analyze real-time network traffic and assess threat severity. This engine may employ various techniques such as pattern recognition, anomaly detection, and heuristic analysis to identify and classify potential security threats as they occur on the network.

[0059] The system 102 may further comprise a vulnerability analysis module 106. The vulnerability analysis module 106 may be configured to assess vulnerabilities of devices and the network as a whole. The vulnerability analysis may consider factors such as software versions, patch levels, known vulnerabilities, and historical security incidents to evaluate the current security posture of individual devices and the overall network infrastructure.

[0060] The system 102 can also include a risk scoring subsystem 108. The risk scoring subsystem 108 may be configured to calculate a cumulative risk score based on the assessed threat severity from the threat assessment engine and the vulnerabilities identified by the vulnerability analysis module. The risk score may provide a quantitative measure of the potential impact of a detected threat in the context of the current network vulnerabilities.

[0061] The system 102 may incorporate a response strategy system 110 configured to activate security measures based on the cumulative risk score. This component may determine the appropriate level of response, which may range from predefined actions for lower risk scenarios to maximum protective measures for high-risk situations. The response strategy system 110 may consult predefined playbooks or employ dynamic decision-making algorithms to select the most suitable security measures.

[0062] In some cases, the system 102 may include a database 114 configured to store historical data on network interactions and system responses. This database 114 may serve as a repository of information about past security incidents, threat patterns, and the effectiveness of various response strategies. The stored data may be used to inform future security decisions and improve the system's overall performance. In some instances, the database 114 can include a listing of rules or policies that each represent potential security measures that can be taken based on the calculated score, such as network packet restriction, device functionality limitations, etc.

[0063] A learning algorithm 112 may be integrated with the database 114 to analyze the historical data and refine the threat detection and response capabilities of the system overtime. This adaptive learning approach may allow the system to evolve and improve its effectiveness in identifying and mitigating new and emerging security threats.

[0064] The system 102 may also feature a user interface 1 16 that allows network administrators (e.g., via user device 122) to monitor system activities, customize security parameters, and manually intervene when necessary. This interface 116 may provide realtime visibility into the network's security status and allow for fine-tuning of the system's automated responses.

[0065] By combining the components as described herein, the system 102 may offer a comprehensive and adaptive approach to network security, capable of responding to a wide range of threats in real-time while continuously learning and improving its protective capabilities.Example Methods

[0066] FIG. 2 is an example flowchart 200 illustrating a method for performing a dynamic network security shielding process. The dynamic network security shielding system 102 as described in FIG. 1 can perform the method as described in FIG. 2.

[0067] At 202, the system can analyze the network traffic to detect one or more threats in the network traffic. The system can implement a machine learning model or neural network to identify threats, such as anomalies in network traffic, malware detected in network devices, blacklisted IP addresses sending network data, etc. The detected threat can be identified by a threat type (e.g., malware, traffic pattern anomaly) and one or more devices vulnerable to the threat (e.g., a server identified as receiving numerous requests indicative of a DDOS attack).

[0068] At 204, the system can determine a severity level of the detected threat. This assessment may involve evaluating the potential impact of the threat on the network and its assets, considering factors such as the type of threat, its known behaviors, and its potential to spread or cause damage. For example, a threat limited to a single computer in the networkmay be identified as having a lower severity level than a network-wide threat to all network devices.

[0069] At 206, the system can identify a vulnerability of one or more devices and the network overall. This can include analyzing software versions, patch levels, historical security incidents, network topology, and known vulnerabilities. This comprehensive evaluation may provide a clear picture of the current security posture of both individual devices and the overall network. For example, a device can be vulnerable based on performance characteristics of the device, whether the device is out of date on software updates, or whether the device is more susceptible to specific threats (e.g., malware threats to an loT device).

[0070] At 208, the system can generate a risk score for the threat. The risk score can incorporate the severity level of the threat along with the identified vulnerability of the devices associated with the threat. This score may serve as a quantitative measure of the potential impact of the detected threat in the context of the current network vulnerabilities.

[0071] At 210, the system can determine whether the risk score is above a threshold. The threshold can include a risk score indicating whether a threat is a severe threat to the network that necessitates a maximum number of protective measures to the network. The threshold can be determined based on previous threats and feedback provided in previous threat mitigation.

[0072] At 212, if the risk score exceeds the threshold, the maximum protective measures are implemented. The maximum protective measures can include a number of measures to the network and devices in the network, such as to limit network traffic or limit functionality of the devices to mitigate the threat. These measures may include actions such as complete network isolation of an affected device, comprehensive port blocking, or immediate deployment of security patches.

[0073] At 214, if the risk score is below the threshold, the system can activate limited predefined actions. In some cases, these predefined actions may include isolating specific network segments, blocking specific network ports for specific IP addresses, or raising alerts for manual intervention. The specific actions taken may depend on the nature and severity of the identified threat. For example, if only a single computer is vulnerable to the threat and the risk score is below the threshold, the only actions can include blocking traffic to the vulnerable device.

[0074] The system may continuously monitor network traffic, allowing for real-time threat detection and response. This continuous monitoring may enable the system to dynamically adjust security measures based on the nature and severity of identified threats and the vulnerabilities of the network and its devices.

[0075] The dynamic network security shielding system may include a threat assessment engine designed to analyze real-time network traffic, detect potential threats, and assess their severity. The engine may employ advanced techniques to identify and classify various types of network threats as they occur. In some cases, the threat assessment engine may utilize machine learning algorithms for detecting and classifying network threats. These algorithms may be trained on large datasets of known threat patterns and behaviors, allowing the engine to recognize both known and threat signatures. The machine learning approach may enable the threat assessment engine to adapt to evolving threat landscapes and improve its detection capabilities over time.

[0076] The threat assessment engine may also incorporate heuristic analysis techniques for threat detection. Heuristic analysis may involve examining network traffic and system behaviors for signs of malicious activity based on predefined rules and patterns. This approach may allow the engine to identify potential threats that do not match known signatures but exhibit suspicious characteristics.

[0077] In some cases, the threat assessment engine may combine multiple analysis techniques to enhance its threat detection capabilities. For example, the engine may use machine learning algorithms to identify anomalous network behavior and apply heuristic analysis to determine if the anomaly represents a genuine security threat.

[0078] The threat assessment engine may continuously monitor network traffic in real-time, analyzing various aspects such as packet contents, traffic patterns, and communication protocols. This ongoing analysis may allow for rapid identification of potential security threats as they emerge.

[0079] Once a potential threat is detected, the threat assessment engine may perform a severity assessment. This assessment may consider factors such as the type of threat, its potential impact on network resources, and its ability to spread or escalate. The severity assessment may provide input for the system's risk scoring mechanism and subsequent response strategy determination.

[0080] In some cases, the threat assessment engine may categorize detected threats based on their characteristics and severity levels. This categorization may assist in prioritizing threats and determining appropriate response measures.

[0081] The threat assessment engine may also be designed to minimize false positives while maintaining high detection rates. This balance may be achieved through continuous refinement of the machine learning models and heuristic rules based on feedback and historical data.

[0082] By employing advanced techniques such as machine learning algorithms and heuristic analysis, the threat assessment engine may provide a robust first line of defense in the dynamic network security shielding system, enabling rapid and accurate identification of potential security threats in real-time network traffic.

[0083] The dynamic network security shielding system may include a vulnerability analysis module designed to continuously assess the vulnerability of network devices and the overallnetwork. This module can play a role in evaluating the current security posture of the network infrastructure and its components. In some cases, the vulnerability analysis module may assess vulnerabilities based on multiple factors. These factors may include software versions, patch levels, historical security incidents, and network topology. By considering these various aspects, the module may provide a comprehensive view of potential weaknesses in the network's defenses. The vulnerability analysis module may examine software versions installed on network devices. This examination may involve comparing the installed versions against known vulnerable versions and identifying any outdated software that may pose security risks.

[0084] In some cases, the module may assess patch levels of operating systems and applications across the network. This assessment may help identify devices with missing security patches, which may be more susceptible to certain types of attacks. The vulnerability analysis module may also consider historical security incidents. By analyzing past security events, the module may identify patterns or recurring vulnerabilities that require attention. This historical analysis may contribute to a more accurate assessment of the network's overall vulnerability.

[0085] Network topology may be another factor considered by the vulnerability analysis module. The module may examine the structure and interconnections of the network to identify potential weak points or areas where security measures may need to be strengthened. This topology analysis may help in understanding how vulnerabilities in one part of the network might affect other areas.

[0086] In some cases, the vulnerability analysis module may use a scoring system to quantify the vulnerability level of individual devices and the network as a whole. This scoring may be based on a weighted combination of the various factors assessed. The vulnerability analysis module may perform its assessments on a continuous basis. This ongoing evaluation may allow for real-time updates to the network's vulnerability profile as changes occur in softwareversions, patch levels, or network topology. By providing a comprehensive and up-to-date assessment of network vulnerabilities, the vulnerability analysis module may contribute information to the overall risk assessment and response strategy of the dynamic network security shielding system.

[0087] The dynamic network security shielding system may incorporate a risk scoring mechanism designed to calculate a cumulative risk score based on inputs from the threat assessment engine and vulnerability analysis module. This risk scoring mechanism may play a role in determining the appropriate response strategy for detected security threats. In some cases, the risk scoring mechanism may combine multiple factors to generate a comprehensive risk assessment. These factors may include the nature and severity of identified threats, as well as the vulnerabilities of the network and its devices. By considering both threat characteristics and system vulnerabilities, the risk scoring mechanism may provide a more accurate representation of the potential impact of a security threat. In some examples, such scores may be compared to a predetermined threshold as described herein.

[0088] The risk scoring mechanism may assign weights to different factors based on their relative importance in assessing overall risk. For example, a highly severe threat may be given more weight in the risk calculation compared to a minor vulnerability. The specific weighting scheme may be customizable to align with the security priorities of the network environment.

[0089] In some cases, the risk scoring mechanism may use a mathematical formula to calculate the cumulative risk score. This formula may involve multiplying the threat severity by the vulnerability level and applying additional modifiers based on other relevant factors. The resulting score may be normalized to fall within a predefined range, such as 0 to 100, for easier interpretation. The risk scoring mechanism may dynamically adjust its calculations based on real-time inputs from the threat assessment engine and vulnerability analysis module. As new threats are detected or vulnerabilities are identified, the risk score may beupdated accordingly. This dynamic adjustment may allow for continuous reassessment of the network's security posture. In some cases, the risk scoring mechanism may incorporate historical data to refine its calculations. By analyzing past security incidents and their outcomes, the mechanism may improve its accuracy in predicting the potential impact of similar threats in the future.

[0090] The cumulative risk score generated by the risk scoring mechanism may serve as a key input for the system's response strategy determination. In some cases, predefined thresholds may be established to trigger different levels of response based on the calculated risk score. For example, a high-risk score exceeding a certain threshold may prompt the activation of maximum protective measures, while a lower score may result in less aggressive security actions. By providing a quantitative measure of risk, the risk scoring mechanism may enable more informed and automated decision-making in response to security threats. This approach may allow for more efficient allocation of security resources and faster response times to potential network breaches.

[0091] In some cases, the risk scoring mechanism may generate detailed reports explaining the factors contributing to the calculated risk score. These reports may provide network administrators with valuable insights into the most significant threats and vulnerabilities affecting their network security.

[0092] The risk scoring mechanism may be designed to be flexible and adaptable to different network environments and security requirements. In some cases, the mechanism may allow for customization of scoring parameters and thresholds to align with specific organizational risk tolerance levels and security policies.

[0093] The dynamic network security shielding system may include a response strategy system configured to activate security measures based on the cumulative risk score calculated by the risk scoring mechanism. This response strategy system may be designed to determine and implement appropriate security actions in response to detected threats and vulnerabilities.In some cases, the response strategy system may utilize predefined thresholds to categorize risk levels and determine the appropriate response. These thresholds may be customizable to align with specific organizational security policies and risk tolerance levels.

[0094] The response strategy system may activate different levels of protective measures based on the calculated risk score. For lower risk scores, the system may implement predefined actions tailored to address specific types of threats or vulnerabilities. These predefined actions may include measures such as temporarily isolating specific network segments, blocking certain network ports for specific IP addresses, or raising alerts for manual intervention by network administrators. In some cases, the predefined actions for lower risk scenarios may be organized into playbooks. These playbooks may outline a series of steps to be taken in response to specific types of threats or vulnerabilities. The response strategy system may consult these playbooks to determine the most appropriate course of action based on the nature of the detected threat and the calculated risk score.

[0095] When the cumulative risk score exceeds a predefined threshold, the response strategy system may activate maximum protective measures. These maximum protective measures may be designed to provide the highest level of security in response to high-risk scenarios. In some cases, the maximum protective measures may include actions such as complete network isolation of affected devices, comprehensive port blocking across the network, or immediate deployment of security patches to vulnerable systems.

[0096] The response strategy system may be capable of implementing multiple protective measures simultaneously or in a specific sequence, depending on the severity and nature of the detected threat. This flexibility may allow for a more comprehensive and effective response to complex security incidents.

[0097] In some cases, the response strategy system may incorporate adaptive learning capabilities. By analyzing the effectiveness of past responses, the system may refine andadapt its response strategies over time. This adaptive approach may enable the system to improve its ability to mitigate threats and reduce the impact of security incidents.

[0098] In some cases, the response strategy system may incorporate a machine learning model to provide adaptive learning capabilities. This model may be designed to analyze historical data on network interactions, security events, and system responses to improve the system's overall performance over time. The machine learning model may utilize techniques such as supervised learning, unsupervised learning, or reinforcement learning.

[0099] The model may process large volumes of data collected from various components of the system, including the threat assessment engine, vulnerability analysis module, and response strategy system. By identifying patterns and correlations in this data, the model may generate insights that can be used to refine threat detection algorithms, adjust risk scoring parameters, and adjust response strategies.

[0100] In some aspects, the machine learning model may employ feature extraction techniques to identify the most relevant characteristics of security events and system responses. These features may be used to train predictive models that can anticipate potential threats or vulnerabilities before they manifest. The model may also utilize clustering algorithms to group similar security incidents, potentially uncovering new threat categories or attack vectors that were previously unrecognized.

[0101] The adaptive learning capabilities provided by the machine learning model may extend to various aspects of the system. For example, the model may continuously refine the weights assigned to different factors in the risk scoring mechanism, ensuring that the calculated risk scores accurately reflect the evolving threat landscape. Similarly, the model may adjust the thresholds used by the response strategy system to trigger different levels of protective measures, balancing security needs with operational efficiency.

[0102] The response strategy system may also provide mechanisms for manual override or intervention by network administrators. This feature may allow human operators to adjust orsupplement the automated responses when necessary, providing an additional layer of control and flexibility in managing network security. By dynamically selecting and activating appropriate security measures based on calculated risk scores, the response strategy system may enable the dynamic network security shielding system to provide a rapid, targeted, and effective response to a wide range of security threats and vulnerabilities.

[0103] The dynamic network security shielding system may incorporate a database and learning algorithm component designed to store historical data on network interactions and system responses. This component may play a role in enhancing the system's adaptive learning capabilities and refining threat detection and response strategies over time. In some cases, the database may serve as a repository for storing comprehensive historical data related to network security events. This data may include information about detected threats, system responses, and the outcomes of those responses. The database may also store details about network interactions, traffic patterns, and system configurations to provide context for security events.

[0104] The learning algorithm may be designed to analyze the stored historical data to identify patterns, trends, and correlations that can inform future security decisions. In some cases, the learning algorithm may employ advanced machine learning techniques to process large volumes of data and extract meaningful insights.

[0105] The database and learning algorithm component may contribute to the system's adaptive learning capabilities in several ways. In some cases, the component may adjust threat detection parameters based on the analysis of historical data. For example, if certain network behaviors consistently correlate with security threats, the learning algorithm may refine the threat detection criteria to improve accuracy and reduce false positives.

[0106] The component may also play a role in optimizing response strategies. By analyzing the effectiveness of previous responses to similar threats, the learning algorithm may suggest adjustments to response thresholds and actions. In some cases, this may involve modifyingthe risk scoring mechanism to better reflect the actual impact of different types of threats based on historical outcomes.

[0107] The database and learning algorithm may enable the system to adapt to evolving threat landscapes. As new types of threats emerge and are recorded in the database, the learning algorithm may update threat detection and classification models to recognize these new patterns. This adaptive approach may help the system stay current with the latest cybersecurity challenges.

[0108] In some cases, the database and learning algorithm component may provide insights for long-term security planning. By analyzing historical trends, the component may identify recurring vulnerabilities or persistent threat vectors, allowing network administrators to implement more targeted and effective security measures.

[0109] The adaptive learning capabilities enabled by the database and learning algorithm may extend to various aspects of the system's operation. For example, the component may refine the vulnerability assessment criteria based on historical data about successful exploits and their impact on different network configurations. In some cases, the database and learning algorithm component may generate reports and visualizations to help network administrators understand long-term security trends and the effectiveness of different security strategies. These insights may inform decision-making about security investments and policy adjustments. The database and learning algorithm may be designed with scalability in mind, capable of handling increasing volumes of data as the network grows or security events accumulate over time. In some cases, the component may employ distributed storage and processing techniques to manage large-scale data analysis efficiently.

[0110] By continuously refining threat detection and response strategies based on historical data and adaptive learning, the database and learning algorithm component may contribute significantly to the overall effectiveness and intelligence of the dynamic network security shielding system. The dynamic network security shielding system may include a userinterface designed to provide network administrators with comprehensive control and visibility over the system's operations. This user interface may serve as a central point for monitoring, customization, and manual intervention in the system's automated processes. In some cases, the user interface may offer real-time monitoring capabilities, allowing administrators to observe the current state of network security. The monitoring features may include visualizations of network traffic patterns, active threats, and ongoing security measures. These visualizations may help administrators quickly assess the network's security posture and identify areas of concern.

[0111] The user interface may provide functionality for customizing various parameters related to threat detection and response. In some cases, administrators may be able to adjust thresholds for risk scoring, modify the weights assigned to different threat factors, or finetune the sensitivity of threat detection algorithms. This customization capability may allow organizations to tailor the system's behavior to their specific security requirements and risk tolerance levels.

[0112] In some cases, the user interface may include tools for defining and managing response playbooks. These playbooks may outline specific sequences of actions to be taken in response to different types of security threats. Administrators may be able to create, edit, and prioritize these playbooks through the user interface, ensuring that the system's automated responses align with organizational security policies and best practices.

[0113] The user interface may also provide mechanisms for manual intervention in the system's automated processes. In some cases, administrators may be able to override automatic responses triggered by the system. This manual override capability may be useful in situations where human judgment is necessary to address complex or unusual security scenarios. In some cases, the user interface may include a dashboard displaying key security metrics and system performance indicators. This dashboard may provide at-a-glanceinformation about the number of detected threats, system response times, and the overall health of the network security infrastructure.

[0114] The user interface may offer detailed logging and reporting features. In some cases, administrators may be able to generate custom reports on security incidents, system responses, and long-term trends. These reports may aid in compliance efforts, security audits, and strategic planning for future security enhancements.

[0115] In some cases, the user interface may include alert management features. Administrators may be able to configure notification settings, specifying which types of security events should trigger alerts and how these alerts should be delivered (e.g., email, SMS, or in-system notifications).

[0116] The user interface may provide access controls to ensure that only authorized personnel can modify system settings or override automated responses. In some cases, the interface may support role-based access control, allowing organizations to define different levels of administrative access based on job responsibilities.

[0117] In some cases, the user interface may offer integration with other security and network management tools. This integration may allow administrators to correlate data from multiple sources and manage various aspects of network security through a single interface. The user interface may include features for system configuration and maintenance. In some cases, administrators may be able to schedule system updates, manage backup and recovery processes, and configure system-wide settings through the interface.

[0118] In some cases, the user interface may provide a simulation environment where administrators can test the impact of configuration changes or new security policies before applying them to the live network. This simulation capability may help reduce the risk of unintended consequences when modifying system settings. The user interface may offer customizable views and layouts, allowing administrators to tailor the interface to theirspecific workflow and preferences. In some cases, this customization may extend to creating personalized dashboards or report templates.

[0119] The simulation environment may provide a platform for administrators to model various network scenarios and security configurations. The simulation environment may allow for the creation of virtual network topologies, simulated traffic patterns, and artificial security threats, enabling administrators to test and refine security strategies without impacting the live network. The simulation may incorporate historical data and machine learning predictions to create realistic scenarios and may provide detailed analytics on the effectiveness of different security measures under various conditions. Administrators may use the simulation environment to evaluate the potential impact of new security policies, assess the system's response to emerging threat vectors, or conduct training exercises for security personnel, all within a controlled and risk-free virtual environment.

[0120] In some cases, the user interface may include collaborative features that allow multiple administrators to work together on security incidents or policy development. These features may include shared workspaces, commenting systems, or real-time collaboration tools. The user interface may provide historical data analysis tools, allowing administrators to review past security events, system responses, and configuration changes. This historical perspective may be valuable for identifying long-term trends and evaluating the effectiveness of security strategies over time.

[0121] The dynamic network security shielding system may be designed to integrate seamlessly with existing network infrastructures and security systems. This integration capability may allow organizations to enhance their current security posture without the need for complete overhauls of their existing network architecture. In some cases, the dynamic network security shielding system may be compatible with a wide range of network devices, protocols, and security appliances. The system may utilize standard network interfaces and protocols to communicate with existing network components, enabling smooth integrationinto diverse network environments. The system may offer flexible deployment options to accommodate various network configurations. In some cases, the system may be deployed as a standalone appliance, while in others, the system may be implemented as a software solution that can be installed on existing network hardware.

[0122] In some cases, the dynamic network security shielding system may provide APIs (Application Programming Interfaces) to facilitate integration with other security tools and management systems. These APIs may allow for data exchange and coordination between the dynamic network security shielding system and other security solutions, such as Security Information and Event Management (SIEM) systems or network monitoring tools.

[0123] The scalability of the dynamic network security shielding system may allow for deployment across networks of varying sizes and complexities. In some cases, the system may be capable of scaling horizontally by adding additional processing nodes to handle increased network traffic and security events in larger network environments. The system may employ distributed architecture to ensure scalability and performance in large-scale deployments. In some cases, this distributed architecture may allow for load balancing and fault tolerance, ensuring consistent performance and reliability even as the network grows.

[0124] In some cases, the dynamic network security shielding system may offer modular components that can be selectively deployed based on the specific needs and scale of the network. This modularity may allow organizations to start with core functionalities and expand the system's capabilities as their network security requirements evolve.

[0125] The system may include built-in performance optimization features to maintain efficiency as the network scales. In some cases, these features may include adaptive resource allocation, where system resources are dynamically assigned based on current network conditions and security priorities. In some cases, the dynamic network security shielding system may support multi-tenancy, allowing for efficient management of security across multiple network segments or organizational units within a larger network infrastructure. Thismulti-tenancy support may be particularly useful for managed service providers or large enterprises with complex organizational structures.

[0126] The system may offer centralized management capabilities to simplify administration across large-scale deployments. In some cases, this centralized management may allow administrators to configure, monitor, and update multiple instances of the system from a single interface, streamlining operations in complex network environments. In some cases, the dynamic network security shielding system may include built-in capacity planning tools to help organizations anticipate and prepare for future scaling needs. These tools may analyze current usage patterns and growth trends to provide recommendations for system expansion or resource allocation.

[0127] The system may support integration with cloud-based services and hybrid network environments. In some cases, this integration may allow organizations to extend the system's protection to cloud-based assets and manage security consistently across on-premises and cloud infrastructures. In some cases, the dynamic network security shielding system may offer customizable data retention and storage options to accommodate varying compliance requirements and data management needs across different network scales and industries.

[0128] The system may include features for automated discovery and integration of new network devices and segments. In some cases, this capability may simplify the process of expanding the system's coverage as the network grows or changes. In some cases, the dynamic network security shielding system may support virtual environments, allowing for deployment in software-defined networks and virtualized infrastructure. This support may provide flexibility in adapting the system to modern network architectures and cloud-native environments. The dynamic network security shielding system may incorporate robust security and reliability features to protect the system itself and ensure continuous operation. These features may be designed to safeguard the integrity of the system's components and the data it processes.

[0129] In some cases, the system may employ advanced encryption techniques to secure all data processed and stored within the system. This encryption may extend to data in transit between system components, as well as data at rest in storage. The encryption algorithms used may be regularly updated to maintain alignment with current industry standards and best practices in cybersecurity.

[0130] The system may implement a comprehensive set of security protocols to protect against unauthorized access and potential attacks targeting the system itself. These protocols may include multi-factor authentication for administrative access, regular security audits, and continuous monitoring for suspicious activities within the system's own processes. In some cases, the dynamic network security shielding system may utilize secure communication channels for all internal and external data transfers. These secure channels may employ protocols such as Transport Layer Security (TLS) to ensure the confidentiality and integrity of data in transit.

[0131] The system may incorporate measures to minimize downtime and ensure high availability. In some cases, these measures may include redundant hardware components, load balancing across multiple servers, and automated failover mechanisms. These features may help maintain system operation even in the event of hardware failures or unexpected spikes in processing demands. In some cases, the system may implement a robust backup and recovery strategy to protect against data loss and enable rapid restoration of services in case of system failures. This strategy may include regular automated backups, offsite data replication, and documented recovery procedures.

[0132] The dynamic network security shielding system may include self-monitoring capabilities to detect and respond to potential issues within its own operations. In some cases, this self-monitoring may involve continuous performance metrics analysis, automated error detection, and proactive alerts to system administrators when potential problems are identified.

[0133] In some cases, the system may employ sandboxing techniques to isolate certain processes or components, providing an additional layer of security against potential vulnerabilities or malicious activities that might target the system itself.

[0134] The system may implement strict access controls and privilege management to limit the potential impact of insider threats or compromised administrative accounts. In some cases, these controls may include the principle of least privilege, where users and processes are granted only the minimum level of access necessary to perform their functions. In some cases, the dynamic network security shielding system may undergo regular security assessments and penetration testing to identify and address potential vulnerabilities. These assessments may be conducted by both internal teams and external security experts to ensure comprehensive evaluation of the system's security posture.

[0135] The system may include features for secure logging and auditing of all system activities, including administrative actions, configuration changes, and security events. In some cases, these logs may be stored in a tamper-evident manner to maintain their integrity for forensic analysis if needed.

[0136] In some cases, the dynamic network security shielding system may implement a formal patch management process to ensure that all system components are kept up-to-date with the latest security patches and updates. This process may include automated patch deployment and validation procedures to minimize potential vulnerabilities introduced by outdated software. The system may incorporate mechanisms for secure remote access and management, allowing administrators to securely monitor and maintain the system from offsite locations. In some cases, these remote access capabilities may be protected by additional security measures such as virtual private networks (VPNs) and time-limited access tokens.

[0137] In some cases, the dynamic network security shielding system may implement data anonymization or pseudonymization techniques for sensitive information processed by the system. These techniques may help protect privacy and reduce the potential impact of databreaches. The system may include features for secure configuration management, ensuring that system settings and parameters are protected against unauthorized changes. In some cases, this may involve version control for configuration files, change approval processes, and automated configuration validation checks.

[0138] In some cases, the dynamic network security shielding system may employ hardware security modules (HSMs) for secure key management and cryptographic operations. These HSMs may provide an additional layer of protection for critical security functions and sensitive data.

[0139] The system may implement measures to protect against side-channel attacks that might attempt to extract sensitive information through analysis of the system's physical characteristics, such as power consumption or electromagnetic emissions. In some cases, these measures may include hardware-level protections and software obfuscation techniques.

[0140] In some cases, the dynamic network security shielding system may include features for secure decommissioning and data destruction when components are retired or replaced. These features may ensure that sensitive data and configuration information are securely erased and cannot be recovered from decommissioned hardware. The system may incorporate resilience against distributed denial-of-service (DDoS) attacks, protecting not only the network it secures but also its own infrastructure. In some cases, this may involve traffic filtering, rate limiting, and the ability to dynamically scale resources to absorb attack traffic.

[0141] In some cases, the dynamic network security shielding system may implement secure coding practices and regular code reviews to minimize the risk of vulnerabilities introduced during system development and updates. These practices may help ensure the overall security and reliability of the system's software components. The dynamic network security shielding system may be applied in various scenarios to detect, respond to, and mitigate different types of network security threats. The following examples illustrate how the system may operate in specific use cases.

[0142] In some cases, the dynamic network security shielding system may detect and respond to a potential Distributed Denial of Service (DDoS) attack. The system may identify abnormal traffic patterns, such as a sudden surge in incoming requests from multiple sources. Upon detection, the system may calculate a risk score based on the volume and nature of the traffic, as well as the current vulnerability status of the targeted network resources. If the risk score exceeds a predefined threshold, the system may activate maximum protective measures. These measures may include temporarily blocking traffic from suspected malicious IP addresses, implementing rate limiting on incoming requests, and redirecting traffic through scrubbing centers to filter out attack traffic.

[0143] The dynamic network security shielding system may also be utilized to identify and isolate a compromised device within a network. In some cases, the system may detect unusual behavior from a specific device, such as attempts to access restricted resources or communication with known malicious IP addresses. The system may evaluate the risk posed by this behavior, considering factors such as the sensitivity of the data accessible to the device and its position within the network topology. Based on the calculated risk score, the system may implement a graduated response. This response may range from increased monitoring and logging of the device's activities to complete network isolation, preventing the compromised device from communicating with other network resources.

[0144] In some cases, the dynamic network security shielding system may be employed to protect against advanced persistent threats (APTs). The system may utilize its machine learning capabilities to identify subtle, long-term patterns of malicious activity that may evade traditional security measures. Upon detecting potential APT activity, the system may adjust its monitoring parameters, increase the frequency of vulnerability assessments for potentially affected systems, and implement additional access controls. The system may also generate detailed reports for security analysts, providing context and historical data to support further investigation and response planning.

[0145] The dynamic network security shielding system may be applied in scenarios involving insider threats. In some cases, the system may detect unusual data access patterns or attempts to escalate privileges beyond normal job requirements. The system may calculate a risk score based on the user's historical behavior, the sensitivity of the accessed data, and the potential impact on the organization. Depending on the risk level, the system may implement responses such as requiring additional authentication factors, limiting access to sensitive resources, or alerting security personnel for immediate investigation.

[0146] In some cases, the dynamic network security shielding system may be utilized to protect against zero-day vulnerabilities. The system may employ its anomaly detection capabilities to identify exploitation attempts of previously unknown vulnerabilities. Upon detecting suspicious activity that does not match known attack patterns, the system may implement precautionary measures such as increased monitoring, temporary access restrictions, and expedited patch management processes. The system may also share anonymized threat data with trusted security partners to contribute to broader threat intelligence efforts.

[0147] The dynamic network security shielding system may be applied in scenarios involving multi-vector attacks. In some cases, the system may detect simultaneous threats across different network segments or attack types. The system may correlate these events to calculate a comprehensive risk score that reflects the potential synergistic impact of multiple attack vectors. Based on this assessment, the system may implement a coordinated response strategy that addresses all identified threat vectors while prioritizing the protection of critical assets and data.

[0148] In some cases, the dynamic network security shielding system may be employed to enhance security during critical software updates or system migrations. The system may adjust its risk thresholds and monitoring parameters to account for potential vulnerabilities introduced during the update process. The system may implement temporary accessrestrictions, increase logging of system changes, and provide real-time alerts to IT personnel about any suspicious activities detected during the update window.

[0149] The dynamic network security shielding system may also be utilized in scenarios involving third-party vendor access to network resources. In some cases, the system may implement specialized monitoring and access controls for external vendors, calculating risk scores based on the vendor's access patterns, the sensitivity of accessible data, and the current threat landscape. The system may dynamically adjust access permissions and implement additional authentication requirements based on real-time risk assessments.

[0150] In some cases, the dynamic network security shielding system may be applied to protect Internet of Things (loT) devices within a network. The system may employ specialized detection algorithms to identify potential compromises of loT devices, which may exhibit different behavior patterns compared to traditional computing devices. Upon detecting suspicious activity, the system may implement targeted responses such as network segmentation to isolate loT devices from critical network resources or firmware update initiation to address potential vulnerabilities.

[0151] The dynamic network security shielding system may be utilized in cloud migration scenarios. In some cases, the system may adapt its monitoring and response strategies to address the unique security challenges of hybrid cloud environments. The system may implement consistent security policies across on-premises and cloud-based resources, adjusting risk calculations to account for the shared responsibility model of cloud security. The system may also provide enhanced visibility into data flows between on-premises and cloud environments, implementing additional security measures for sensitive data traversing public networks.

[0152] The dynamic network security shielding system can provide a significant advancement in network security, offering a comprehensive and adaptive approach to protecting digital infrastructures against evolving cyber threats. By combining real-timethreat detection, vulnerability assessment, and dynamic response strategies, the system can provide a robust defense mechanism capable of addressing a wide range of security challenges.

[0153] In some cases, the system may store historical data on network interactions and system responses. This stored historical data may be utilized to refine future security measures through adaptive learning processes. By analyzing past security events and the effectiveness of previous responses, the system may continuously improve its threat detection capabilities and adjust its response strategies.

[0154] The system may employ machine learning algorithms for pattern recognition and anomaly detection when identifying and classifying potential security threats. These advanced algorithms may enable the system to recognize both known threat signatures and novel attack patterns, enhancing its ability to detect and respond to emerging security risks.

[0155] In some cases, the dynamic network security shielding system may be implemented by executing instructions stored on a non-transitory computer-readable medium. This implementation approach may allow for flexible deployment across various hardware platforms and network environments.

[0156] Future enhancements to the dynamic network security shielding system may include integration with emerging technologies such as artificial intelligence for more sophisticated threat prediction and automated response optimization. The system may also be extended to address security challenges in evolving network paradigms, such as 5G networks and edge computing environments.

[0157] In some cases, future variations of the system may incorporate enhanced capabilities for securing Internet of Things (loT) devices and industrial control systems, addressing the unique security challenges posed by these specialized network components. The system may also be adapted to provide more granular protection for cloud-native applications and microservices architectures.

[0158] In some aspects, the system may include industry-specific modules tailored to address the unique security requirements of sectors such as healthcare, finance, and critical infrastructure. These specialized modules may incorporate domain-specific threat intelligence and compliance requirements. For example, in the healthcare sector, domain-specific threat intelligence may include information about recent ransomware attacks targeting medical devices or electronic health record systems, while compliance requirements may involve adherence to HIPAA regulations for protecting patient data. In another example, for the financial industry, threat intelligence may focus on emerging tactics used in financial fraud or cryptocurrency theft, and compliance requirements may include meeting Payment Card Industry Data Security Standard (PCI DSS) standards for secure payment processing. In the realm of critical infrastructure, domain-specific threat intelligence may encompass information about state-sponsored cyber attacks on power grids or water treatment facilities, while compliance requirements may involve adherence to North American Electric Reliability Corporation Critical Infrastructure Protection (NERC CIP) standards for securing industrial control systems.

[0159] In some cases, future iterations of the system may feature advanced visualization and reporting tools, providing network administrators with more intuitive interfaces for understanding complex security events and long-term trends. These enhancements may facilitate more effective decision-making and strategic planning for network security.

[0160] The dynamic network security shielding system may continue to evolve, adapting to the changing landscape of cyber threats and network technologies. By maintaining a flexible and extensible architecture, the system may remain at the forefront of network security, providing organizations with a powerful tool for safeguarding their digital assets and operations.Example Computer Devices

[0161] FIG. 3 shows an example computing device 300 which may be used in the systems and methods described herein. In the example computer 300 a CPU or processor 310 is in communication by a bus or other communication 312 with a user interface 314. The user interface includes an example input device 316 such as a keyboard, mouse, touchscreen, button, joystick, or other user input device(s). The user interface 314 also includes a display device 318 such as a screen. The computing device 300 shown in FIG. 3 also includes a network interface 320 which is in communication with the CPU 320 and other components. The network interface 320 may allow the computing device 300 to communicate with other computers, databases, networks, user devices, or any other computing capable devices. In some examples, additionally or alternatively, the method of communication may be through WIFI, cellular, Bluetooth Low Energy, wired communication, or any other kind of communication. In some examples, additionally or alternatively, the example computing device 300 includes peripherals also in communication with the processor 310. In some examples, additionally or alternatively, peripherals include stage motors such as electric servo and / or stepper motors used for moving the probe up and down. In some example computing devices 300, a memory 322 is in communication with the processor 310. In some examples, additionally or alternatively, this memory 322 may include instructions to execute software such as an operating system 332, network communications module 334, other instructions 336, applications 338, applications to control the spectrometer and / or light source 340, applications to process data 342, data storage 358, data such as data tables 360, transaction logs 362, sample data 364, sample location data 330 or any other kind of data.Conclusion

[0162] As disclosed herein, features consistent with the present inventions may be implemented by computer- hardware, software and / or firmware. For example, the systems and methods disclosed herein may be embodied in various forms including, for example, a dataprocessor, such as a computer that also includes a database, digital electronic circuitry, firmware, software, computer networks, servers, or in combinations of them. Further, while some of the disclosed implementations describe specific hardware components, systems and methods consistent with the innovations herein may be implemented with any combination of hardware, software and / or firmware. Moreover, the above-noted features and other aspects and principles of the innovations herein may be implemented in various environments. Such environments and related applications may be specially constructed for performing the various routines, processes and / or operations according to the invention or they may include a general-purpose computer or computing platform selectively activated or reconfigured by code to provide the necessary functionality. The processes disclosed herein are not inherently related to any particular computer, network, architecture, environment, or other apparatus, and may be implemented by a suitable combination of hardware, software, and / or firmware. For example, various general-purpose machines may be used with programs written in accordance with teachings of the invention, or it may be more convenient to construct a specialized apparatus or system to perform the required methods and techniques.

[0163] Aspects of the method and system described herein, such as the logic, may be implemented as functionality programmed into any of a variety of circuitry, including programmable logic devices (“PLDs”), such as field programmable gate arrays (“FPGAs”), programmable array logic (“PAL”) devices, electrically programmable logic and memory devices and standard cell-based devices, as well as application specific integrated circuits. Some other possibilities for implementing aspects include: memory devices, microcontrollers with memory (such as EEPROM?), embedded microprocessors, firmware, software, etc. Furthermore, aspects may be embodied in microprocessors having software -based circuit emulation, discrete logic (sequential and combinatorial), custom devices, fuzzy (neural) logic, quantum devices, and hybrids of any of the above device types. The underlying device technologies may be provided in a variety of component types, e.g., metal-oxidesemiconductor field-effect transistor (“MOSFET”) technologies like complementary metal- oxide semiconductor (“CMOS”), bipolar technologies like emitter-coupled logic (“ECL”), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures), mixed analog and digital, and so on.

[0164] It should also be noted that the various logic and / or functions disclosed herein may be enabled using any number of combinations of hardware, firmware, and / or as data and / or instructions embodied in various machine-readable or computer-readable media, in terms of their behavioral, register transfer, logic component, and / or other characteristics. Computer- readable media in which such formatted data and / or instructions may be embodied include, but are not limited to, non-volatile storage media in various forms (e.g., optical, magnetic or semiconductor storage media) and carrier waves that may be used to transfer such formatted data and / or instructions through wireless, optical, or wired signaling media or any combination thereof. Examples of transfers of such formatted data and / or instructions by carrier waves include, but are not limited to, transfers (uploads, downloads, e-mail, etc.) over the Internet and / or other computer networks by one or more data transfer protocols (e.g., HTTP, FTP, SMTP, and so on).

[0165] Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in a sense of “including, but not limited to.” Words using the singular or plural number also include the plural or singular number respectively. Additionally, the words “herein,” “hereunder,” “above,” “below,” and words of similar import refer to this application as a whole and not to any particular portions of this application. When the word “or” is used in reference to a list of two or more items, that word covers all of the following interpretations of the word: any of the items in the list, all of the items in the list and any combination of the items in the list.

[0166] Although certain presently preferred implementations of the invention have been specifically described herein, it will be apparent to those skilled in the art to which the invention pertains that variations and modifications of the various implementations shown and described herein may be made without departing from the spirit and scope of the invention. Accordingly, it is intended that the invention be limited only to the extent required by the applicable rules of law.

[0167] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.

[0168] As used in this document, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Those having skill in the art can also translate from the plural form to the singular as is appropriate to the context and / or application. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. Nothing in this disclosure is to be construed as an admission that the embodiments described in this disclosure are not entitled to antedate such disclosure by virtue of prior invention. As used in this document, the term “comprising” means “including, but not limited to.”

[0169] It will be understood by those within the art that, in general, terms used herein are generally intended as “open” terms (for example, the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” et cetera). Whilevarious compositions, methods, and devices are described in terms of “comprising” various components or steps (interpreted as meaning “including, but not limited to”), the compositions, methods, and devices also can “consist essentially of’ or “consist of’ the various components and steps, and such terminology should be interpreted as defining essentially closed-member groups.

[0170] In addition, even if a specific number is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (for example, the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, et cetera” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (for example, “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, et cetera). In those instances where a convention analogous to “at least one of A, B, or C, et cetera” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (for example, “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, et cetera). It will be further understood by those within the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description, sample embodiments, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

[0171] In addition, where features of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0172] As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, et cetera. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, et cetera. As will also be understood by one skilled in the art all language such as “up to,” “at least,” and the like include the number recited and refer to ranges that can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth.

[0173] The term “about,” as used herein, refers to variations in a numerical quantity that can occur, for example, through measuring or handling procedures in the real world; through inadvertent error in these procedures; through differences in the manufacture, source, or purity of compositions or reagents; and the like. Typically, the term “about” as used herein means greater or lesser than the value or range of values stated by 1 / 10 of the stated values, e.g., ±10%. The term “about” also refers to variations that would be recognized by one skilled in the art as being equivalent so long as such variations do not encompass known values practiced by the prior art. Each value or range of values preceded by the term “about” is also intended to encompass the embodiment of the stated absolute value or range of values. Whether or not modified by the term “about,” quantitative values recited in the present disclosure include equivalents to the recited values, e.g., variations in the numerical quantityof such values that can occur, but would be recognized to be equivalents by a person skilled in the art.

[0174] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

CLAIMSWhat is claimed:

1. A computer-implemented method performed by a dynamic network security shielding system comprising: receiving a set of network traffic in a computer network that comprises a set of computing nodes; analyzing the network traffic to detect a threat; determining a severity level for each detected threat; evaluating a vulnerability of at least a first computing node in the set of computing nodes and a communications network that the first computing node is connected; generating a risk score that is based on a combination of the determined severity level of the threat and the evaluated vulnerability of at least the first computing node and the communications network; and responsive to the generated risk score exceeding a threshold, implementing a maximum number of security actions to the set of computing nodes and the communications network; or responsive to the generated risk score not exceeding a threshold, implementing a limited number of predefined actions specific to the first computing node and / or the communications network.

2. The computer-implemented method of claim 1, wherein analyzing the network traffic is performed by a machine learning model configured to identify one or more patterns in the network traffic and identify anomalies from the identified patterns that are indicative of the detected threat.

3. The computer- implemented method of claim 1, wherein evaluating the vulnerability of at least the first computing node and the communications network comprises analyzing a software version of the first computing node, identify a software patch level of the firstcomputing node, identifying a historical security incidents relating to the first computing node or the communications network, identifying a topology of the communications network, and identifying one or more known vulnerabilities of the first computing node and / or the communications network.

4. The computer-implemented method of claim 1, wherein the limited number of predefined actions include at least one of: isolating one or more network segments of the first computing node, blocking one or more network ports associated with a subset of internet protocol (IP) addresses, and generating an alerts for manual intervention that specifies the alert and the evaluated vulnerability of the first computing node .

5. The computer-implemented method of claim 1, further comprising: storing a set historical data relating to network interactions and system responses in a database.

6. The computer-implemented method of claim 5, further comprising: detecting a second threat in the network traffic; processing the stored historical data to identify a result of implementing the maximum number of security actions or the limited number of predefined actions; and refining a risk score generated for the second threat based on the identified result of implementing the maximum number of security actions or the limited number of predefined actions in response to the threat.

7. The computer-implemented method of claim 6, wherein a set of threat detection parameter and response thresholds are generated from the identified result of implementing the maximum number of security actions or the limited number of predefined actions in response to the threat.

8. A network security system comprising: a threat assessment engine configured to: analyze a set of network traffic to detect a threat; anddetermine a severity level of the detected threat; a vulnerability analysis module configured to estimate a vulnerability of at least a first computing node and a communications network; a risk scoring subsystem configured to generate a risk score for the threat based on the determined severity level of the threat and the vulnerability of at least the first computing node and the communications network; and a response strategy system configured to activate one or more security measures based on the risk score.

9. The network security system of claim 8, wherein the threat assessment engine is configured to implement a machine learning model to detect the threat from the set of network traffic.

10. The network security system of claim 8, wherein the estimated vulnerability of the first computing node and the communications network is based on any of a software version of the first computing node, a patch level of the first computing node, a set of historical security incidents of the first computing node or the communications network, and a topology of the communications network.

11. The network security system of claim 8, wherein the response strategy system is configured to implement a maximum set of protective measures when the generated risk score exceeds a predefined threshold.

12. The network security system of claim 11, wherein the maximum set of protective measures include at least one of: a complete network isolation of at least the first computing node, comprehensive port blocking of at least the first computing node, and immediate deployment of security patches to at least the first computing node.

13. The network security system of claim 8, further comprising a database configured to store historical data on network interactions and system responses.

14. The network security system of claim 13, wherein the response strategy system is further configured to utilize the stored historical data to refine future response strategies through adaptive learning.

15. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for adapting network security responses, the method comprising: receiving a set of network traffic in a computer network that comprises a set of computing nodes; analyzing the network traffic to detect a threat; determining a severity level for each detected threat; evaluating a vulnerability of at least a first computing node in the set of computing nodes and a communications network that the first computing node is connected; generating a risk score that is based on a combination of the determined severity level of the threat and the evaluated vulnerability of at least the first computing node and the communications network; and responsive to the generated risk score exceeding a threshold, implementing a maximum number of security actions to the set of computing nodes and the communications network; or responsive to the generated risk score not exceeding a threshold, implementing a limited number of predefined actions specific to the first computing node and / or the communications network.

16. The non-transitory computer-readable medium of claim 15, wherein analyzing the network traffic is performed by a machine learning model configured to identify one or more patterns in the network traffic and identify anomalies from the identified patterns that are indicative of the detected threat.

17. The non-transitory computer-readable medium of claim 15, wherein evaluating the vulnerability of at least the first computing node and the communications network comprises analyzing a software version of the first computing node, identify a software patch level of the first computing node, identifying a historical security incidents relating to the first computing node or the communications network, identifying a topology of the communications network, and identifying one or more known vulnerabilities of the first computing node and / or the communications network.

18. The non-transitory computer-readable medium of claim 15 , wherein the limited number of predefined actions include at least one of: isolating one or more network segments of the first computing node, blocking one or more network ports associated with a subset of internet protocol (IP) addresses, and generating an alerts for manual intervention that specifies the alert and the evaluated vulnerability of the first computing node .

19. The non-transitory computer-readable medium of claim 15, wherein the instructions further cause the processor to perform the method comprising: storing a set historical data relating to network interactions and system responses in a database.

20. The non-transitory computer-readable medium of claim 19, wherein the instructions further cause the processor to perform the method comprising: detecting a second threat in the network traffic; processing the stored historical data to identify a result of implementing the maximum number of security actions or the limited number of predefined actions; and refining a risk score generated for the second threat based on the identified result of implementing the maximum number of security actions or the limited number of predefined actions in response to the threat.

Images