Reinforcement Learning Vulnerability Detection System
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Solution Overview
Problem
Existing methods for detecting system vulnerabilities, particularly application memory vulnerabilities in x86 architecture, are time-consuming and often misleading due to reliance on static heuristic-based attacks or pseudo-random payload generation.
Innovation Solution
A system utilizing a machine learning model trained on an action space to generate and iteratively update payloads for testing vulnerabilities, leveraging reinforcement learning to optimize payload generation based on system resource utilization and reward scores, thereby improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static heuristic-based attacks or pseudo-random payload generation are used to detect vulnerabilities, then the detection process is simple to implement, but the time consumption increases significantly
Solution Approach 1:
The patent transforms the static payload generation process into a dynamic reinforcement learning system. The machine learning model continuously learns from system responses and adapts its payload generation strategy, enabling it to efficiently identify vulnerabilities without relying on time-consuming static heuristic methods or random generation.
Solution Approach 2:
The patent replaces traditional mechanical payload generation methods (heuristic-based attacks and pseudo-random generation) with an intelligent machine learning system. This substitution enables the system to automatically learn optimal attack strategies and significantly reduce detection time while maintaining ease of implementation through automated processes.
2Ease of manufacture
If static heuristic-based attacks or pseudo-random payload generation are used to detect vulnerabilities, then the implementation approach is straightforward, but the accuracy of vulnerability detection becomes misleading
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model receives system responses to generated payloads and uses this information to iteratively improve its detection accuracy. The model learns from successful and unsuccessful attacks, refining its strategy to accurately identify vulnerabilities without being misled by false positives or negatives.
Solution Approach 2:
The patent replaces straightforward but inaccurate static heuristic methods with an intelligent machine learning system that can adapt and learn from system responses. This substitution maintains implementation simplicity while dramatically improving detection accuracy through automated learning and optimization.
3Productivity
If machine learning model with reinforcement learning is used to generate payloads, then the time consumption and resource consumption are significantly reduced, but the system complexity increases
Solution Approach 1:
The patent implements a self-service machine learning system that automatically generates payloads, learns from system responses, and optimizes its detection strategy without requiring complex external configuration or management. The reinforcement learning model autonomously improves its performance, reducing both time consumption and resource requirements while managing system complexity internally.
4Use of energy by moving object
If traditional payload generation methods are used, then the system resource utilization is lower, but the vulnerability detection effectiveness is reduced
Solution Approach 1:
The patent employs a dynamic machine learning system that adapts its resource utilization based on learning progress and detection needs. The reinforcement learning model optimizes payload generation to achieve high detection effectiveness while efficiently managing system resources, avoiding both waste and insufficient utilization.
Data Source
AI summary
A system and method for testing system vulnerabilities are provided. The method may include: training, by a processor, a machine learning model and agent to generate a payload to test vulnerabilities in the application by: selecting one or more input data from the action space to form an action data; electronically providing the action data as an input to the application; monitoring utilization of one or more system resources in response to the action data being inputted to the application; determining a score of utilization based on a result of the monitoring; determining a corresponding reward score for the action data based on the score of utilization; and identifying one or more of the action data to form a pool of candidate data for the application.