Autonomous Agent Reinforcement Learning for Anti-Malware Detection
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Solution Overview
Problem
Current anti-malware software relies on static detection rules that become inconsistent and ineffective due to the constant renewal of malware, leading to inadequate detection and prevention of malicious software.
Innovation Solution
A method using a reinforcement learning algorithm to train an autonomous agent that modifies malware content to make it appear benign to anti-malware software, thereby improving detection performance without requiring frequent updates to static detection rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static detection rules are updated regularly, then malware detection capability is improved, but the number of detection rules increases making consistency problematic
Solution Approach 1:
The patent transitions from static detection rules to a dynamic reinforcement learning system where an autonomous agent continuously learns and adapts detection strategies. The agent dynamically adjusts its behavior based on rewards from detection outcomes, eliminating the need for manual rule updates and maintaining consistency without increasing rule complexity.
Solution Approach 2:
The autonomous agent performs self-learning and self-optimization through reinforcement learning. It automatically improves detection capability by receiving rewards for successful detections and penalties for false positives, eliminating the need for external expert intervention to update rules while maintaining system reliability.
2Ease of manufacture
If static detection rules are used, then implementation is simple, but detection effectiveness decreases against renewed malware
Solution Approach 1:
The patent replaces the mechanical system of static rule-based detection with an intelligent system using reinforcement learning. The autonomous agent learns optimal detection strategies through interaction with the environment, substituting fixed mechanical rules with adaptive intelligent behavior that maintains both simplicity and effectiveness.
Solution Approach 2:
The system changes from fixed detection parameters (static rules) to dynamic parameters that adapt through reinforcement learning. The agent continuously adjusts its detection behavior based on rewards, allowing the system to maintain effectiveness against new malware while keeping the underlying implementation relatively simple.
3Adaptability or versatility
If more detection rules are added, then coverage of malware types increases, but the time and expert knowledge required for maintenance increases
Solution Approach 1:
The autonomous agent automatically adapts to new malware types through self-learning via reinforcement learning. When encountering new malware patterns, the agent explores actions, receives rewards for successful detections, and updates its policy accordingly, eliminating the need for expert time to manually add and maintain detection rules for each new malware variant.
Solution Approach 2:
The system implements continuous feedback loops where the autonomous agent receives reward signals from detection outcomes. This feedback mechanism allows the agent to automatically learn from successes and failures, adapting to new malware types without external intervention and reducing maintenance time while increasing adaptability.
Data Source
AI summary
A method for training an autonomous agent implementing a reinforcement learning algorithm to improve the performance of anti-malware software. The method includes: selecting an action aimed at modifying the content of a malware; transmitting the selected action to an environment implementing said anti-malware software; receiving, from the environment, a reward representing a probability that the malware modified by application of the selected action is considered benign by the anti-malware software; obtaining a state representative of the malware modified by application of the selected action; the steps of selecting an action, receiving a reward and obtaining a state being iterated as long as a stopping criterion is not reached; and, determining, by the reinforcement learning algorithm and based on the obtained rewards, a function which associates with each state at least one action to be executed, so as to maximize a sum of the obtained rewards.