Autonomous Security Testing Agent for SIEM Vulnerability Detection
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Solution Overview
Problem
Current SIEM system testing methods, such as penetration testing, rely heavily on human labor and are prone to test bias and inefficiency, especially in dynamic environments where machine learning models continuously change, making it difficult to ensure accurate and comprehensive vulnerability identification.
Innovation Solution
An autonomous security testing agent (STA) utilizing reinforcement learning to continuously learn and test attack strategies within a SIEM system, simulating legitimate user actions to identify vulnerabilities and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If penetration testing is used to test SIEM system, then vulnerability identification can be achieved, but the testing process relies heavily on human labor and is prone to test bias
Solution Approach 1:
The security testing system performs self-service through autonomous agents that automatically conduct penetration testing without human intervention. The agents learn from reward feedback loops and continuously improve their testing capabilities, eliminating reliance on human operators while maintaining high accuracy in vulnerability identification.
Solution Approach 2:
The patent replaces the mechanical human-operated penetration testing system with an automated intelligent agent system. These agents use reinforcement learning to substitute human intuition and decision-making with algorithmic processes that can objectively evaluate SIEM systems without human bias.
2Reliability
If conventional penetration testing is used, then testing can be performed, but it is inefficient in dynamic environments where machine learning models continuously change
Solution Approach 1:
The security testing agents are designed to be dynamic and adaptive, continuously learning from the environment and adjusting their strategies. The reinforcement learning models enable the agents to evolve their testing approaches in response to changing SIEM systems, maintaining reliability in dynamic environments where traditional static testing methods fail.
Solution Approach 2:
The system implements continuous feedback loops where agents receive reward signals from their testing actions and use this feedback to update their policies. This feedback mechanism enables the agents to adapt to dynamic environments and improve testing efficiency over time, addressing the inefficiency of conventional methods in changing conditions.
3Measurement precision
If human-based penetration testing is used, then security alerts can be evaluated, but it is difficult to ensure accurate and comprehensive vulnerability identification
Solution Approach 1:
The testing system is segmented into multiple autonomous agents, each capable of independent vulnerability detection. This segmentation allows the system to comprehensively evaluate different aspects of the SIEM system simultaneously, improving detection accuracy while distributing the computational complexity across multiple specialized agents rather than requiring a single complex human-operated system.
Data Source
AI summary
A system and method for identifying vulnerabilities in a security information and event management (SIEM) system. A method includes: initializing a security testing agent with a goal and a reinforcement learning model, wherein the model defines states indicative of progress towards the goal, a set of actions that can be taken by a legitimate user within a target environment, and reward values associated with taking a specified action in a specified state; and learning a policy to achieve the goal within the target environment, wherein learning: selects and takes a target action from the set of actions for a current state; monitors for an alert triggered in response to the target action being taken; receives a reward value associated with the target action and current state; calculates updated reward value in the model; and in response to the process not being terminated, repeats the process for a next state.