Remote cyber / network security monitoring and diagnostic system using machine learning

Abstract

A system for remote monitoring and diagnostics of cyber and network security, consisting of: a plurality of endpoint agents (201) configured to collect system and network behavior data; a cloud-based aggregation module (202) configured to receive and preprocess said data; a machine learning engine (203) comprising unsupervised and supervised models for detecting anomalies and classifying threats; and a remote dashboard (204) for visualizing security events and diagnostics in real time.

Description

Field of the Invention:The present invention relates to the field of network and cyber security. More particularly, it relates to a system and method for remotely monitoring and diagnosing cyber threats and network susceptibility using machine learning algorithms. The invention utilizes advanced data analytics, anomaly detection, and real-time pattern recognition to provide proactive and adaptive cybersecurity management across distributed digital infrastructures.BACKGROUND OF THE INVENTION;In the developing digital environment, organizations and governments are increasingly relying on networked systems to manage critical operations. The emerging of cloud computing, Internet of Things (IoT), and mobile platforms has led to the spread of complex, distributed networks that cover wide geographic areas and support numerous applications. With this expansion, the area of attack for potential cyber attacks has exponentially increased. Companies are faced with a variety of security challenges ranging from malware and ransomware attacks via insider threats, zero day vulnerabilities to advanced persistent threshs (APTs). Ensuring real-time transparency and threat detection in such bulky and dynamic digital infrastructures is a tremendous challenge.Traditional cyber security tools are often rule-based and rely heavily on predefined signatures and static heuristics. While these techniques can efficiently detect known threats, they are substantially ineffective against novel or sophisticated attacks that develop in an unpredictable manner. Moreover, these systems typically require constant human supervision and frequent manual updates to remain relevant in view of new threats. As digital traffic volume increases and threat designers have refined, it is clear that static rule-based approaches are no longer sufficient to protect modern networks.Another critical limitation of conventional cybersecurity solutions is their lack of scalability and adaptability. Most existing systems are designed for field use, making them less suitable for organizations operating in multi-site or cloud-based environments. Such architectures result in latencies and inefficiencies in data acquisition, analysis, and response. They also suffer from fragmented visibility because different tools can monitor different parts of the infrastructure without significant integration or coordination.At the same time, the volume, variety and speed of data generated in digital ecosystems present both challenge and chance. While the massive data stream may easily overmanage traditional systems, it also provides a rich source of information that, when properly analyzed, may provide implementable awareness for threat detection and network diagnostics. Machine learning (ML) technologies are particularly important here. ML algorithms can analyze large amounts of structured and unstructured data, recognize patterns, and adapt to new inputs without requiring explicit reprogramming. They can detect anomalies, predict results, and recommend reactions with far greater speed and accuracy than manual methods.Despite these advantages, current ML-based security systems often do not achieve comprehensive protection. Many are stand-alone tools that are not integrated into a more comprehensive remotely controllable architecture. They typically require large amounts of historical tagged data to function effectively and have difficulty maintaining performance in view of rapidly changing network behavior. Moreover, these tools become static and ineffective over time without suitable mechanisms for continuous learning and remote provisioning.There is a clear need for a smart, scalable, and remotely accessible cyber security system that utilizes the adaptive machine learning capabilities to provide proactive monitoring and diagnostic services. Such a system should be able to collect and analyze data from distributed endpoints in real time, recognize both known and unknown threats, and provide clear diagnostic insight into safety incidents. Moreover, it must be securely operated via the cloud, integrate into the existing security infrastructure, and be able to adapt to new threat patterns and behaviors.The present invention responds to this need by introducing a comprehensive system for remote monitoring and diagnosis of cyber and network security using machine learning. It combines multi-layered data acquisition, adaptive ML algorithms, real-time analyses, and remote visualization capabilities to provide a consistent and smart security solution. The system provides administrators and security experts with the necessary tools to proactively understand, predict, and respond to threats, all within a scalable cloud-integrated framework.Summary of the Invention:The present invention provides a comprehensive system for remote monitoring and diagnosis of cyber and network security using machine learning. It is designed to overcome the limitations of traditional cyber security solutions by combining cloud-based infrastructure, real-time analyses, and adaptive learning models into a uniform framework. This system allows organizations to continuously monitor and diagnose safety threats in distributed environments at non-exemplary speed, accuracy, and scalability.In essence, the invention integrates lightweight software agents distributed across various endpoints within a target network, such as servers, routers, user devices, IoT sensors, and virtual machines. These agents are responsible for collecting a variety of operational data, including, but not limited to, network packet metadata, login attempts, system protocols, application behavior, file access protocols, and user interaction patterns. This data is securely encrypted and transmitted in real time to a central processing module in the cloud.The cloud-based processing unit performs multiple critical functions. First, it aggregates and preprocesses the incoming data streams, removes noise, and extracts meaningful features that can be analyzed by machine learning models. Next, it employs a series of supervised and unsupervised learning algorithms to detect and classify safety events. Unsupervised methods, such as isolation forests and autoencoders, identify anomalies based on deviations from normal behavior. Monitored models trained on labeled past attack data sets are used to categorize known threats such as malware infections, brute force attacks, or lateral movements within the network.A prominent feature of the invention is its continuous learning capability. The system includes a feedback loop by which safety analyzers can validate or correct the results of the system. These inputs are used to update the underlying models stepwise so that they can adapt to the unique behaviors and threat profiles of each environment over time. This dynamic learning process is further enhanced by the optional use of federated learning that allows model enhancements to be shared across distributed networks without jeopardizing sensitive local data.In addition to the detection, the invention offers comprehensive diagnostic tools. These include cause analysis, behavior tracking, and predictive trend analysis. For example, if an anomaly is detected, the system may track the sequence of events that have caused the users or devices involved to identify and evaluate whether the anomaly was part of a larger coordinated attack. Administrators can access these findings via an intuitive dashboard that displays real-time analyses, visualizations, and alarm summary. Customizable settings allow users to set risk thresholds, automate responses, and incorporate alerts into external security information and event management (SIEM) systems.Security and compliance are anchored throughout the system. All communication channels use modern encryption protocols such as TLS 1.3 and all data is anonymous and leased to ensure privacy. Access to the monitoring console is controlled by multi-factor authentication and role-based permissions. The system also creates testing protocols for forensic analyses and regulatory reporting to assist in compliance with data protection standards such as GDPR, HIPAA, and NIST frameworks.The invention is modular and expandable. Organizations may implement them in a single location or at multiple remote locations, step by step or fully. It supports hybrid cloud environments and is capable of integrating with various third party security tools via standardized APIs. This flexibility makes it ideal for companies, government authorities, healthcare providers, financial institutions, and industrial operators who require comprehensive and intelligent cyber defense mechanisms.By combining machine learning, remote accessibility, and real-time diagnostic capabilities, the system provides a powerful solution to modern cybersecurity challenges. It allows organizations to transition from reactive to proactive threat management and provides robust protection against both known and emerging threats in complex and evolving digital landscapes.Brief Description of the DrawingsFigure 1 shows a block diagram of the system of the invention.DETAILED DESCRIPTION OF THE INVENTIONThe invention disclosed herein relates to an intelligent, adaptive system capable of performing comprehensive monitoring and diagnostic operations in the area of cyber and network security, specifically designed for use in distributed and remote environments. The system utilizes machine learning capabilities to autonomously detect, classify, and react to security threats in real-time, and provides a proactive approach to cyber defense in complex, multi-layered digital ecosystems.The basic architectural concept underlying the invention is the integration of remote data acquisition with centralized machine learning based analysis, which is orchestrationd by a cloud based infrastructure that enables remote access and monitoring. Within the core of this system is a network of software agents deployed in the monitored environment. These agents are lightweight in design and optimized to run on a variety of devices, including, but not limited to, user terminals, network routers, servers, cloud-hosted entities, and IoT endpoints. Its primary object is to collect a continuous stream of operational and behavioral data which is then securely transmitted to a cloud processing layer for central aggregation and evaluation.These data streams include various categories of information such as network traffic metadata, system protocols, access control events, process execution lanes, file system changes, user interaction sequences, and interprocess communication. The software agents are designed to locally preprocess this data to reduce noise and minimize bandwidth consumption. This preprocessing stage includes time stamp normalization, personal data anonymousization (PII), local anomaly characterization, and secure encryption using protocols common in the industry such as TLS 1.3 prior to transmission.Once transmitted, the data converges within a secure cloud-based aggregation framework designed for high throughput and low latency data collection. This layer performs stream-based preprocessing, including normalization of heterogeneous data formats, correlation of time matched events, feature extraction, and transformation into structured formats suitable for machine learning. The system uses a combination of time-series structuring and vector space modelling to prepare data for different models in the analytic kernel.The heart of the invention is a smart machine learning engine that performs both anomaly detection and threat classification. The system employs a hybrid modeling approach by combining unsupervised learning to detect pattern variations with supervised learning to accurately identify known attack signatures. The unsupervised layer functions without prior labeling of the data and is particularly effective in discovering subtilous and novel anomalies that deviate from established standards. Models such as autoencoders that reconstruct expected behavior sequences are used to identify events that are outside the reconstruction tolerances. Similarly, isolation forests and cluster algorithms allow the system to distinguish between normal and divergent behaviors based on learned distributions and inter-data relationships.In addition to anomaly detection capability, there are a number of monitored models trained on kuratted data sets that include historical incidents, known threat vectors, and documented system responses. These models, including decision trees, ensemble classifiers, and deep learning architectures, are continuously trained and retrained with labeled data sets augmented by expert feedback and verified incidents. As a result, the system is able to accurately classify security events such as Phishing attempts, ransomware attacks, lateral movements in internal networks, port scans, and Denial of Service activities. In addition, transformer-based models of natural language processing are used to semantically analyze textual protocols and input to the command line, enabling contextual interpretation of suspect script executions or Privilegieneskalationsbefehle.A prominent feature of the invention is its capability for continuous learning and self-adaptation. The system incorporates a dynamic feedback mechanism in which human analyzers or automated scripts can validate, conflict, or refine the system's threat scores. These inputs are fed back to the model lifecycle pipeline, which supports incremental retraining using online learning algorithms. This design ensures that the system continues to develop in response to environmental changes, variations in user behavior, and emerging threat vectors without requiring complete redesign of the model. To support data protection in distributed environments, the system optionally integrates federated learning frameworks that enable edge-level local training, sharing only model weights and gradients with the central model, and thus eliminating the need to expose raw data.Diagnosis is another essential function of the system. Once a security incident is detected, the system automatically initiates a series of forensic analysis procedures. These include identifying the entry point of the incident, tracking the propagation path, isolating affected entities, and determining the schedule of the attack. This diagnostic process is enhanced by the ability of the system to generate behavioral graphs illustrating interactions between users, devices, and processes over time. Such graphics help safety experts visualize potential kill chains and attack surfaces, thereby enabling precise containment and mitigation strategies.All monitoring, threat and diagnostic reports are accessible via a secure web-based interface that acts as a central management console. This user interface, designed for both technical analyzers and guide staff, provides a comprehensive real-time dashboard that displays network health indicators, ongoing incidents, statistical trends, and implementable insights. The dashboard is modular and allows users to configure views based on organizational roles or functional requirements. Users can initiate policy changes, view audit records, and configure automated responses to specific event triggers.The system supports automation by allowing administrators to set conditional response rules based on threat classifications, severity levels, and criticality of assets. For example, an anomaly associated with a privilege user accessing a critical file repository outside business hours could automatically trigger a two-factor authentication challenge, alert security team, and temporarily restrict access to the endpoint involved. Such response flows are managed by an integrated rule engine that supports conditional logic and integrations with external orchestration platforms.With respect to integration, the system is designed to operate in accordance with the existing cyber security infrastructure. It offers APIs and connectors for widely used SIEM, SONAR, IAM and endpoint protection systems that allow data exchange, incident correlation and uniform response. Protocols and threat data may be exported in formats compatible with compliance standards, including JSON, XML, and Syslog protocols, which facilitates test path creation and regulatory reporting. Moreover, the system has been developed with respect to modularity so that organizations can provide it incrementally - starting with selected network segments and extend coverage over time as needed.From a safety point of view, the invention keeps track of a zero trust approach. All internal system communications are authenticated and encrypted, and all changes to the system configuration are logged in an invariable fashion. The system uses role-based access control (RBAC) and forces multi-factor authentication (MFA) for all administrative interfaces. Configuration and event records are stored in cryptographically verifier memory, thereby ensuring that no tampering or deletion of records remains unnoticed.To ensure high availability and resiliency, the invention supports deployment in fault tolerant cloud environments with redundant data centers. The system architecture is containerized, enabling scalable deployment over orchestration platforms such as Kubernetes. Load balancing mechanisms distribute processing to model inference nodes to maintain consistent performance during peak traffic conditions. Data repository policies are customizable such that organizations may set the duration and amount of historical data storage according to regulatory requirements or storage constraints.This invention addresses a critical need in modern cybersecurity management: the ability to remotely and intelligently detect, diagnose and respond to threats in real time. Its hybrid learning models allow to function effectively in both static and dynamic environments, while its remote capabilities ensure that it can be provided and monitored from any location. By incorporating advanced machine learning techniques into a cloud-native secure framework, the invention provides a future-oriented solution to the increasingly complex landscape of network and cyber threats. It not only improves situation awareness in the digital infrastructure, but also enables security teams with the necessary tools to be preventively, minimize risks and maintain system integrity.The combination of scalable architecture, adaptive intelligence and diagnostic depth redefines the system, which is possible in the field of remote monitoring of cybersecurity. It is not only a reactive mechanism, but rather a dynamic digital asset monitor that is capable of developing with its environment and predicting threats before they manifest themselves. This invention thus establishes a new paradigm for how cybersecure systems should function in a age where threats are persistent, data distributed, and networks are constantly changing.List of reference characters200 System 201 Plurality of endpoint agents 202 Cloud-based aggregation module 203 Machine learning engine 204 Remote dashboard

Claims

A system for remote monitoring and diagnosis of cyber and network security, comprising: a plurality of endpoint agents (201) configured to collect system and network behavior data; a cloud-based aggregation module (202) configured to receive and pretreat said data; a machine learning engine (203) comprising unsupervised and monitored models for detecting anomalies and for classifying threats; and a remote dashboard (204) for visualizing security events and diagnoses in real time.The system of claim 1, wherein the machine learning engine comprises autoencoder and isolation forests configured to detect deviations from learned behavior baselines.The system of claim 1, wherein the monitored models are trained on labeled historical incident data and include decision trees, gradient enhanced classifiers, and neural networks.The system of claim 1, wherein the remote dashboard provides forensic diagnostics, real-time threat alerts, and automated response configurations.The system of claim 1, further comprising a reinforcement learning module configured to optimize mitigation strategies based on a reward function associated with system stability and threat mitigation.The system of claim 1, wherein the endpoint agents locally pre-process the data by anonymousization, encryption, and compression prior to secure transmission to the cloud.

Images