Systems and methods to integrate cyber threat intelligence for detection and response of cyber threats

Abstract

Endpoint system comprising an endpoint agent configured for deployment on a computing machine, a backend server adapted to curate and update intelligence fingerprints, a communication module within sa

Description

FIELD The present invention relates generally to cybersecurity and, more specifically, to systems and methods for integrating cyber threat intelligence to detect and respond to cyber threats. BACKGROUND In an increasingly digitalized world, the security of computer networks and endpoints has become paramount. As cyber threats grow in complexity and frequency, organizations are in constant search of effective solutions to protect their digital assets. Traditional approaches to threat detection and response primarily revolve around aggregating various intelligence feeds through Threat Intelligence Platforms (TIPs). These platforms, when combined with Security Information and Event Management (SIEM) systems, aim to capture logs, and correlate them with known Indicators of Compromise (loCs). Upon detecting a match, these systems generate alerts for further investigation. However, there are several limitations and challenges associated with this traditional approach: Quality of Intelligence: Intelligence feeds, in many cases, contain loCs that may not be up-to-date, relevant, or accurate. This can result in numerous false positives, diverting resources from genuine threats. Log Dependency: Many solutions heavily rely on logs. These logs can be resourceintensive, pose challenges in terms of data collection, storage, and management, and can be targets for tampering or deletion by adversaries. Time Constraints: The time taken to investigate each alert can introduce significant delays, during which an organization remains vulnerable. Lack of Integration and Automation: Not every organization is equipped with a SIEM or TIP. Without these platforms, many systems lack the proactive measures needed to counteract sophisticated threats. Moreover, integration complexities can hamper the deployment of effective cybersecurity solutions. Endpoint Detection Limitations: In scenarios lacking an Endpoint Detection and Response (EDR) solution, the capacity to consume intelligence across the network and the ability to respond to identified threats become constrained. Given the above challenges, there is a clear need for a more efficient, less resourceintensive solution capable of providing timely and accurate threat intelligence and response. Ideally, such a solution would not be heavily reliant on logs and would offer both scalability and adaptability to suit varying organizational needs. SUMMARY In general terms, the present disclosure is directed to providing guidance on the patent application process and protecting an invention. Advantageously, the invention addresses the challenges of cyber threat intelligence consumption, such as quality of intelligence, time lag, lack of integration and automation, intelligence collection, endpoint detection, and compromised network logs, by introducing an endpoint solution that offers real-time, automated, and logless threat intelligence and response, focusing on zero-day indicators and adversary intelligence, and operating independently of traditional security tools. According to an aspect of the invention, there is provided an endpoint intelligence system. The system comprises an endpoint agent configured for deployment on a computing machine, a backend server adapted to curate and update intelligence fingerprints, a communication module within said endpoint agent enabling it to request and receive the latest intelligence fingerprints from said backend server, and a mechanism within said endpoint agent to directly analyse network packet details. Optionally, the endpoint agent employs "logless" technology, allowing for the efficient processing and analysis of data without the overhead of traditional logging mechanisms. Optionally, the mechanism for direct network packet analysis is structured to provide insights into network traffic and behaviour patterns, including identifying unusual data transfers, anomalous access patterns, and potential data exfiltration attempts. Optionally, the endpoint system is configured to identify adversary indicators, such as unusual user agent strings, uncommon IP addresses, or patterns consistent with command-and-control servers. Optionally, the endpoint system is configured for zero-day intelligence analysis, enabling it to detect threats not yet indexed in traditional signature databases, including new malware strains or novel exploitation techniques. Optionally, the endpoint system further includes an automated response system integrated within the endpoint agent. The automated response system is capable of actions such as isolating the affected machine from the network, or alerting administrators in real-time. Optionally, the endpoint system operates independently of threat intelligence platforms and security information and event management systems, allowing organizations without these platforms or those seeking to reduce platform redundancies to seamlessly integrate endpoint intelligence into their cybersecurity architecture. Optionally, the endpoint system is configured to be adapted and scaled based on organizational needs, such as deploying additional endpoint agents dynamically as new machines are added to the network or enhancing the backend server capabilities in response to an increase in network traffic. Optionally, the endpoint system uses the mechanism to directly analyze network packet details to detect threats that involve lateral movement and network-based attacks. Optionally, the backend server is equipped with filtering capabilities to prioritize intelligence feeds and rank them based on source credibility, freshness of information, or relevance to the organization's specific industry. According to an aspect of the invention, there is provided a method for threat detection and response using an endpoint (endpoint) system. The method comprises: (a) deploying an endpoint agent on a computing machine; (b) updating said endpoint agent with intelligence fingerprints via communication with a backend server; and (c) directly analysing network packet details using said endpoint agent. Optionally, the endpoint agent employs a "logless" approach during operations, resulting in minimal system overhead and reduced data storage requirements. Optionally, the method further comprises extracting insights from network traffic and behaviour patterns through direct network packet analysis, including flagging patterns indicative of data exfiltration or lateral movement. Optionally, the endpoint system focuses on identifying adversary indicators, such as spotting command and control server communications or detecting patterns consistent with ransomware / criminal / cyber espionage activity. Optionally, the method implements zero-day intelligence analysis to enhance threat detection capabilities, allowing for the proactive detection of emerging threats that aren't yet catalogued in common databases. Optionally, the method further involves initiating an automated response upon threat detection using the integrated response system of the endpoint agent, such as quarantining suspicious files, terminating potentially malicious processes, or triggering multi factor authentication challenges. Optionally, the method operates the endpoint system without the involvement of threat intelligence platforms (TIPS) and security information and event management (SIEM) systems. Optionally, the method adapts and scales the endpoint system operations based on the detected threat landscape. This may include dynamically adjusting the frequency of endpoint agent updates in response to an uptick in network threats or deploying more agents in an expansive network environment. Optionally, the method detects threats that manifest as lateral movement and networkbased attacks through the analysis of network packet details, thereby enabling early detection of silent intruders trying to expand their foothold within the organization's network. Optionally, the method filters and prioritizes intelligence feeds using the backend server of the endpoint system, utilizing criteria such as trustworthiness of the intelligence source, relevance of the threat intelligence to the industry, or the freshness of the threat data. BRIEF DESCRIPTION OF THE DRAWINGS The above and other objects and advantages of the invention of the disclosures will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings in which: Figure 1 illustratively shows an endpoint intelligence system according to the present disclosure. Figure 2 illustratively shows an endpoint intelligent agent as part of the system of Figure 1. Figure 3 illustratively shows a flowchart for performing a method for threat detection and response using an endpoint system according to the present disclosure. DETAILED DESCRIPTION Figure 1 illustratively shows an endpoint system according to the present disclosure. The endpoint system comprises an endpoint agent configured for deployment on a computing machine, a backend server adapted to curate and update intelligence fingerprints, a communication module within the endpoint agent enabling it to request and receive the latest intelligence fingerprints from the backend server, and a mechanism within the endpoint agent to directly analyse network packet details. Advantageously, the endpoint system as described in figure 1 addresses the problem of endpoint threat detection in traditional cyber threat intelligence consumption. The endpoint agent, deployed on a computing machine, allows organizations to consume intelligence across their network and respond to threats identified by intelligence. The backend server, adapted to curate and update intelligence fingerprints, ensures the gathering of the most relevant information to protect an organization. The communication module within the endpoint agent enables it to request and receive the latest intelligence fingerprints from the backend server, allowing the system to tie together communication chains related to adversary behaviour. The mechanism within the endpoint agent to directly analyse network packet details allows the system to provide real-time threat hunting as part of post-incident activities even if the logs are deleted or tampered with. As shown in Figure 1, network packets are received by an active network adaptor. The device may comprise one or more active network adaptors and one or more inactive network adaptors at any given time. Data received by an active network adaptor is funnelled through a network analyser. Multiple data streams can be handled by the network analyser simultaneously to check versions via a version check mechanism and to identify threat intelligence fingerprints from a database via a communications module. The communications module is in constant communication with a messaging handler. In the event that a threat fingerprint is identified, the messaging handler sends an isolate command to the Endpoint agent. Upon sending of an isolate command the data stream is isolated and one or more additional actions may be instigated, as described below. If no isolate command is issued, the data stream is released. Figure 2 illustratively shows a more detailed representation of the endpoint system of Figure 1. As can be seen, the network analyser additionally transmits threat information to a repository of network traffic. This may trigger one or more post-incident activities and inclusion of the threat data into a database of threat fingerprints. This database may be known as threat telemetry. In some embodiments, the endpoint system is configured for zero-day intelligence analysis, enabling it to detect threats not yet indexed in traditional signature databases, including new malware strains or novel exploitation techniques. Advantageously, this configuration allows the endpoint system to identify previously unknown threats and vulnerabilities, thereby enhancing the system's proactive capabilities. The zero-day intelligence analysis is achieved by the endpoint agent periodically requesting the latest intelligence fingerprints from the backend server. This allows the system to swiftly detect newly introduced malicious fingerprints or to increase the accuracy of existing patterns, thereby addressing the problem of quality of intelligence in traditional cyber threat intelligence consumption. In some embodiments, the endpoint system further comprises an automated response system integrated within said endpoint agent, capable of actions including isolating the affected machine from the network, initiating a system scan, or alerting administrators in real-time. Advantageously, this integration provides a direct line for immediate automated response, including device isolation, to stop an attack from progressing and to contain potential malware. This addresses the problem of time lag in traditional systems, as the automated response system can act swiftly upon detection of a threat, reducing the time required for detection and investigation. In some embodiments, the endpoint system operates independently of threat intelligence platforms and security information and event management systems, allowing organizations without these platforms or those seeking to reduce platform redundancies to seamlessly integrate endpoint into their cybersecurity architecture. Advantageously, this independence reduces complexity and cost for organizations that may not have these systems in place or prefer a simpler approach. This addresses the problem of lack of integration and automation in traditional cyber threat intelligence consumption, as the endpoint system can function effectively without reliance on other technologies such as a SIEM, EDR or threat intelligence platform. In some embodiments, the endpoint system is configured to be adapted and scaled based on organizational needs, such as deploying additional endpoint agents dynamically as new machines are added to the network or enhancing the backend server capabilities in response to an increase in network traffic. Advantageously, this scalability makes the endpoint system adaptable to the needs of different organizations, including those with limited resources. This addresses the problem of intelligence collection in traditional systems, as the endpoint system can dynamically adjust its capabilities to ensure the gathering of the most relevant information to protect an organization. In some embodiments, the endpoint system uses a mechanism to directly analyse network packet details to detect threats that involve lateral movement and networkbased attacks. Advantageously, this network-level analysis provides insights into network traffic and behaviour patterns that may not be visible through traditional systems alone. This addresses the problem of endpoint detection in traditional cyber threat intelligence consumption, as the endpoint system can identify advanced threats that involve lateral movement and network-based attacks. In some embodiments, the endpoint system is equipped with filtering capabilities to prioritize intelligence feeds, and ranking them based on source credibility, freshness of information, or relevance to the organization's specific industry. Advantageously, this filtering capability ensures the relevance, accuracy, and timeliness of the intelligence feeds, thereby reducing the rate of false positives. This addresses the problem of quality of intelligence in traditional cyber threat intelligence consumption, as the endpoint system can prioritize, and rank intelligence feeds based on various factors to ensure the most relevant and accurate information is used for threat detection. Figure 3 illustratively shows a flowchart for performing a method for threat detection and response using an endpoint system according to the present disclosure. The method includes three steps: deploying an endpoint agent on a computing machine, updating said endpoint agent with intelligence fingerprints via communication with a backend server, and directly analysing network packet details using said endpoint agent. Advantageously, this method allows organizations to consume intelligence across their network and respond to threats identified by intelligence. The endpoint agent, deployed on a computing machine, allows for real-time threat hunting as part of post-incident activities even if the logs are deleted or tampered with. This addresses the problem of endpoint detection in traditional cyber threat intelligence consumption, as the endpoint system can provide real-time threat hunting capabilities. In some embodiments the endpoint agent employs a "logless" approach during operations, resulting in minimal system overhead and reduced data storage requirements. Advantageously, the "logless" approach of the endpoint agent, as described in the present disclosure, allows organizations to consume intelligence without the need for other technologies such as a SIEM, EDR or threat intelligence platform. This significantly reduces the time required to detect and investigate a detection, thereby addressing the issue of time lag in traditional systems. The "logless" approach also reduces system overhead and data storage requirements, making the endpoint agent more efficient and lightweight. In some embodiments the method further comprises extracting insights from network traffic and behaviour patterns through direct network packet analysis, including flagging patterns indicative of data exfiltration or lateral movement. Advantageously, the direct network packet analysis allows the endpoint agent to gain insights into network traffic and behaviour patterns that may not be visible through traditional EDR solutions alone. This enables the endpoint agent to detect advanced threats that involve lateral movement and network-based attacks, thereby addressing the issue of intelligence integration and automation. The direct network packet analysis also allows the endpoint agent to flag patterns indicative of data exfiltration, providing an additional layer of security. In some embodiments the endpoint system focuses on identifying adversary indicators, including spotting command and control server communications, or detecting patterns consistent with adversary activity. Advantageously, the focus on identifying adversary indicators allows the endpoint system to provide a more streamlined and efficient means of threat detection. The endpoint system can spot command and control server communications or detect patterns consistent with adversary activity, thereby addressing the issue of quality of intelligence. This targeted approach ensures that relevant, accurate, and timely intelligence is integrated, thereby reducing risk . In some embodiments the method implements zero-day intelligence analysis to enhance threat detection capabilities, allowing for the proactive detection of emerging threats that aren't yet catalogued in common databases. Advantageously, the implementation of zero-day intelligence analysis allows the endpoint system to identify previously unknown threats, thereby addressing the issue of lack of integration and automation in traditional cyber threat intelligence consumption. The zero-day intelligence analysis also allows the endpoint system to proactively detect emerging threats that aren't yet catalogued in common databases, making the system proactive rather than reactive. In some embodiments the method further comprises initiating an automated response upon threat detection using the integrated response system of the endpoint agent, including automatically quarantining suspicious files, terminating potentially malicious processes, or triggering multi-factor authentication challenges. Advantageously, the integrated response system of the endpoint agent allows for immediate automated response, including device isolation, to stop an attack from progressing and to contain potential malware. This addresses the problem of intelligence integration into cyber security solutions while endpoint detection in traditional cyber threat intelligence consumption. The automated response system can automatically quarantine suspicious files, terminate potentially malicious processes, or trigger multi-factor authentication challenges, providing a direct line for immediate response. In some embodiments the method operates the endpoint system without the involvement of TIPS and SIEM systems. Advantageously, the independence of the endpoint system from TIPS and SIEMS reduces complexity and cost for organizations that may not have these systems in place or prefer a simpler approach. This addresses the problem of lack of integration and automation in traditional cyber threat intelligence consumption. In some embodiments the method adapts and scales the endpoint system operations based on the detected threat landscape, such as dynamically adjusting the frequency of endpoint agent updates in response to an uptick in network threats or deploying more agents in an expansive network environment. Advantageously, the adaptability and scalability of the endpoint system operations allow it to respond to changes in the threat landscape, thereby addressing the issue of intelligence collection. The endpoint system can dynamically adjust the frequency of endpoint agent updates in response to an uptick in network threats or deploy more agents in an expansive network environment, ensuring the gathering of the most relevant information to protect an organization. In some embodiments the method detects threats that manifest as lateral movement and network-based attacks through the analysis of network packet details, enabling early detection of silent intruders trying to expand their foothold within the organization's network. Advantageously, the ability of the endpoint system to detect threats that manifest as lateral movement and network-based attacks addresses the problem of endpoint detection in traditional cyber threat intelligence consumption. The analysis of network packet details enables early detection of silent intruders trying to expand their foothold within the organization's network, providing real-time threat hunting as part of post-incident activities even if the logs are deleted or tampered with. In some embodiments, the method includes filtering and prioritizing intelligence feeds using the backend server of the endpoint system, utilizing criteria including trustworthiness of the intelligence source, relevance of the threat intelligence to the industry, or the freshness of the threat data. Advantageously, this embodiment addresses the problem of quality of intelligence in traditional cyber threat intelligence consumption. The backend server of the endpoint system, which curates and updates intelligence fingerprints, now also filters and prioritizes intelligence feeds. This is done based on several criteria, such as the trustworthiness of the intelligence source, the relevance of the threat intelligence to the industry, and the freshness of the threat data. This filtering and prioritizing process ensures that the most relevant and reliable intelligence feeds are used, thereby reducing the rate of false positives, and increasing the accuracy and timeliness of threat detection. This embodiment leverages the principles of data analysis and machine learning to filter and prioritize intelligence feeds, thereby enhancing the overall quality of intelligence. In some embodiments, the endpoint agent is configured to periodically request the latest intelligence fingerprints from the backend server. This allows the endpoint agent to swiftly detect newly introduced malicious fingerprints or to increase the accuracy of existing patterns. Advantageously, the method of the present disclosure addresses the problem of time lag and lack of integration in traditional cyber threat intelligence consumption. The endpoint agent, installed on a machine, is configured to periodically request the latest intelligence fingerprints from the backend server. This enables the endpoint agent to swiftly detect newly introduced malicious fingerprints or to increase the accuracy of existing patterns. This embodiment leverages the principles of network communication and real-time data processing to ensure that the endpoint agent is always updated with the latest threat intelligence, thereby reducing the time required to detect and investigate a detection and making the system proactive rather than reactive. In some embodiments, the endpoint agent is configured to provide an immediate automated response, including device isolation, to stop an attack from progressing and to contain potential malware. Advantageously, this embodiment addresses the problem of lack of automation in traditional cyber threat intelligence consumption. The endpoint agent, installed on a machine, is configured to provide an immediate automated response, including device isolation, to stop an attack from progressing and to contain the machine from malicious communications. This embodiment leverages the principles of automated systems and network security to ensure that threats are swiftly and effectively dealt with, thereby reducing the organization's risk exposure, and enhancing its ability to respond to threats identified by intelligence. In some embodiments, the endpoint agent is configured to report network traffic to the backend in near real-time intervals, allowing the system to tie together communication chains related to adversary behaviour. Advantageously, this embodiment addresses the problem of intelligence collection in traditional cyber threat intelligence consumption. The endpoint agent, installed on a machine, is configured to report network traffic to the backend in near real-time intervals. This allows the system to tie together communication chains related to adversary behaviour, thereby enhancing the system's ability to gather the most relevant information to protect an organization. This embodiment leverages the principles of network communication and real-time data processing to ensure that the system is always updated with the latest threat intelligence, thereby enhancing the system's ability to detect and respond to threats. In some embodiments, the method includes filtering and prioritizing intelligence feeds using the backend server of the endpoint system, utilizing criteria including trustworthiness of the intelligence source, relevance of the threat intelligence to the industry, or the freshness of the threat data. Advantageously, this embodiment addresses the problem of quality of intelligence in traditional cyber threat intelligence consumption. The backend server of the endpoint system, which curates and updates intelligence fingerprints, now also filters and prioritizes intelligence feeds. This is done based on several criteria, such as the trustworthiness of the intelligence source, the relevance of the threat intelligence to the industry, and the freshness of the threat data. This filtering and prioritizing process ensures that the most relevant and reliable intelligence feeds are used, thereby reducing the rate of false positives and increasing the accuracy and timeliness of threat detection. This embodiment leverages the principles of data analysis and machine learning to filter and prioritize intelligence feeds, thereby enhancing the overall quality of intelligence. In some embodiments, the endpoint agent is configured to periodically request the latest intelligence fingerprints from the backend server. This allows the endpoint agent to swiftly detect newly introduced malicious fingerprints or to increase the accuracy of existing patterns. Advantageously, the method of the present disclosure addresses the problem of time lag and lack of integration in traditional cyber threat intelligence consumption. The endpoint agent, installed on a machine, is configured to periodically request the latest intelligence fingerprints from the backend server. This enables the endpoint agent to swiftly detect newly introduced malicious fingerprints or to increase the accuracy of existing patterns. This embodiment leverages the principles of network communication and real-time data processing to ensure that the endpoint agent is always updated with the latest threat intelligence, thereby reducing the time required to detect and investigate a detection and making the system proactive rather than reactive. In some embodiments, the endpoint agent is configured to provide an immediate automated response, including device isolation, to stop an attack from progressing and to contain potential malware. Advantageously, this embodiment addresses the problem of lack of automation in traditional cyber threat intelligence consumption. The endpoint agent, installed on a machine, is configured to provide an immediate automated response, including device isolation, to stop an attack from progressing and to contain the machine from malicious communications. This embodiment leverages the principles of automated systems and network security to ensure that threats are swiftly and effectively dealt with, thereby reducing the organization's risk exposure and enhancing its ability to respond to threats identified by intelligence. It will be understood that the term “logless” in relation to the endpoint threat intelligence agent as used herein may refer to a system, method, or process that does not record or maintain a log of its operations or activities, often used in the context of digital or computer-based inventions to enhance privacy and security. It will be understood that the term "threat intelligence platform" as used herein may refer to a software-driven system developed to accumulate, organize, and analyse data pertaining to potential or active cyber threats. The platform often integrates diverse intelligence sources, including but not limited to public feeds, proprietary databases, and human insights. It will be understood that the term "Security Information and Event Management" or "SIEM" as used herein may refer to an integrated software solution or set of applications designed to provide real-time analysis of security alerts generated by diverse hardware and software infrastructure components within an organization. SIEM systems aggregate log data produced by numerous sources, such as network devices, security appliances, databases, and operating systems, to provide a holistic view of an organization's information security. The system might also pertain to event correlation, where disparate log entries can be linked to identify patterns indicative of a security breach or other malicious activity. It will be understood that the term "Indicators of Compromise" or "loC" as used herein may refer to artifacts or evidence that suggest a computer system or network has been exposed to a security incident or cyber intrusion. It will be understood that the term "Endpoint Detection and Response" or "EDR" as used herein may refer to a cybersecurity solution specifically tailored to monitor, detect, and respond to malicious activities and threats on endpoint devices, such as desktops, laptops, mobile devices, and servers. EDR systems constantly gather and analyse endpoint data to identify unusual patterns or behaviours that might signify a breach or intrusion. These systems are designed not only to detect threats but also to provide a set of response tools that allow cybersecurity professionals to isolate affected devices, investigate the root cause, and remediate the issue. The foregoing disclosure provides illustration and description but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications may be made considering the above disclosure or may be acquired from practice of the implementations. As used herein, the term “component” is intended to be broadly construed as hardware, firmware, or a combination of hardware and software. It will be apparent that systems and / or methods described herein may be implemented in different forms of hardware, firmware, and / or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code - it being understood that software and hardware can be used to implement the systems and / or methods based on the description herein. As used herein, satisfying a threshold may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, and / or the like, depending on the context. Although combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of various implementations includes each dependent claim in combination with every other claim in the claim set. No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more.” Further, as used herein, the article “the” is intended to include one or more items referenced in connection with the article “the” and may be used interchangeably with “the one or more.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, and / or the like), and may be used interchangeably with “one or more.” Where only one item is intended, the phrase “only one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and / or,” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of’).

Claims

What is claimed is:

1. An Endpoint (ENDPOINT) system comprising:a. an ENDPOINT Agent configured for deployment on a computing machine;b. a backend server adapted to curate and update intelligence fingerprints;c. a communication module within said ENDPOINT Agent enabling it to request and receive the latest intelligence fingerprints from said backend server; andd. a mechanism within said ENDPOINT Agent to directly analyse network packet details.

2. The ENDPOINT system of claim 1, wherein said ENDPOINT Agent employs "logless" technology, allowing for the efficient processing and analysis of data without the overhead of traditional logging mechanisms.

3. The ENDPOINT system of claim 1, wherein said mechanism for direct network packet analysis is structured to provide insights into network traffic and behaviour patterns, including identifying unusual data transfers, and potential data exfiltration attempts.

4. The ENDPOINT system of claim 1, configured to identify adversary indicators, including, but not limited to, unusual user agent strings, uncommon IP addresses, or patterns consistent with command-and-control servers.

5. The ENDPOINT system of claim 1, configured for zero-day intelligence analysis, enabling it to detect threats not yet indexed in traditional signature databases, including new malware strains or novel exploitation techniques.

6. The ENDPOINT system of claim 1, further comprising an automated response system integrated within said ENDPOINT Agent, capable of actions including isolating the affected machine from the network, or alerting administrators in real-time.Intelligence Platforms (TIPs) and Security Information and Event Management (SIEM) systems allowing organizations without these platforms or those seeking to reduce platform redundancies to seamlessly integrate ENDPOINT into their cybersecurity architecture.

8. The ENDPOINT system of claim 1, configured to be adapted and scaled based on organizational needs, such as deploying additional ENDPOINT Agents dynamically as new machines are added to the network or enhancing the backend server capabilities in response to an increase in network traffic.

9. The ENDPOINT system of claim 1, wherein the mechanism to directly analyze network packet details is used to detect threats that involve lateral movement and network-based attacks.

10. A method for threat detection and response using an Endpoint Threat Intelligence (ENDPOINT) system, comprising:a. Deploying an ENDPOINT Agent on a computing machine;b. Updating said ENDPOINT Agent with intelligence fingerprints via communication with a backend server; andc. Directly analysing network packet details using said ENDPOINT Agent.

11. The method of claim 11, wherein said ENDPOINT Agent employs a "logless" approach during operations, resulting in minimal system overhead and reduced data storage requirements.

12. The method of claim 11, further comprising extracting insights from network traffic and behaviour patterns through direct network packet analysis, including flagging patterns indicative of data exfiltration or lateral movement.

13. The method of claim 11, wherein the ENDPOINT system focuses on identifying adversary indicators, including spotting command and control server communications or detecting patterns consistent with adversary activity.enhance threat detection capabilities, allowing for the proactive detection of emerging threats that aren't yet catalogued in common databases.

15. The method of claim 11, further comprising initiating an automated response upon threat detection using the integrated response system of the ENDPOINT Agent, including automatically quarantining suspicious files, terminating potentially malicious processes, or triggering multi-factor authentication challenges.16.The method of claim 11, operating the ENDPOINT system without the involvement of Threat Intelligence Platforms (TIPs) and Security Information and Event Management (SIEM) systems.

17. The method of claim 11, adapting and scaling the ENDPOINT system operations based on the detected threat landscape, such as dynamically adjusting the frequency of ENDPOINT Agent updates in response to an uptick in network threats or deploying more agents in an expansive network environment.

18. The method of claim 11, detecting threats that manifest as lateral movement and network-based attacks through the analysis of network packet details, enabling early detection of silent intruders trying to expand their foothold within the organization's network.

Images