Reinforcement Learning Agents for Cybersecurity Graph Vulnerability Detection
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Solution Overview
Problem
Existing cybersecurity measures struggle to quickly identify and mitigate vulnerabilities in computer networks, leading to potential data breaches and attacks.
Innovation Solution
The implementation of reinforcement learning with navigational constraints applied to a cybersecurity graph to determine the potential for attackers to traverse the network, identifying high-risk nodes and vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vulnerability management methods are used to identify and classify vulnerabilities, then comprehensive vulnerability tracking is achieved, but the response time to emergent threats is too slow
Solution Approach 1:
The patent replaces traditional mechanical vulnerability scanning and classification systems with reinforcement learning agents that operate on cybersecurity graphs. These agents use graph neural networks to automatically identify vulnerabilities and predict attack paths, substituting manual or rule-based mechanical processes with intelligent automated systems that respond in real-time to emerging threats.
Solution Approach 2:
The system changes the operational parameters of vulnerability management by transitioning from periodic scanning to continuous real-time analysis. The reinforcement learning agents continuously monitor the cybersecurity graph, dynamically updating vulnerability assessments and attack path predictions as new information becomes available, thereby reducing response time while maintaining identification accuracy.
2Measurement precision
If comprehensive vulnerability scanning of all network assets is performed, then complete vulnerability coverage is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent segments the network into a cybersecurity graph where assets, vulnerabilities, and attack paths are represented as discrete nodes and edges. Reinforcement learning agents operate on this segmented graph structure, allowing selective and targeted analysis of specific network portions rather than uniform scanning of all assets, thereby reducing computational overhead while maintaining comprehensive coverage.
Solution Approach 2:
The system creates a virtual copy of the network in the form of a cybersecurity graph that mirrors the actual network structure and vulnerability relationships. The reinforcement learning agents analyze this graph copy to identify vulnerabilities and predict attack paths, eliminating the need for repeated actual network scanning and reducing computational resource consumption while maintaining complete vulnerability coverage.
3Loss of time
If reinforcement learning agents are deployed to identify attack paths, then rapid threat response is achieved, but system complexity increases
Solution Approach 1:
The patent introduces an environment manager as an intermediary layer between the reinforcement learning agents and the cybersecurity graph. This manager coordinates multiple agents, manages their interactions with the graph, and synthesizes their outputs into actionable security insights. The intermediary structure organizes the complexity of multiple agents and their interactions, making the overall system more manageable while preserving the rapid threat response capability.
Data Source
AI summary
Methods, systems and computer program products are provided for integrating risk and threat intelligence from various sources, to provide real-time awareness of potential threats to a computer network, which are now described herein in terms of an example enterprise system.