Reinforcement-Learned Cyberattack Sequences for Realistic Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cyberwarfare training systems face limitations in generating realistic and varied cyberattack scenarios due to reliance on expert knowledge and simplified simulations, leading to suboptimal training outcomes and a shortage of skilled cybersecurity professionals.
Innovation Solution
A method and apparatus for generating a cyberattack sequence using reinforcement learning, which includes configuring a cyberattack simulation environment, training a cyberattack agent model, and generating an attack sequence, utilizing a network model, action space, state space, and reward function to enhance simulation accuracy and applicability to real environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simplified simulation environments are used to train cyberattack agents, then training efficiency is improved, but simulation accuracy and applicability to real environments deteriorate
Solution Approach 1:
The simulation environment dynamically adjusts its complexity based on training requirements. The system can switch between simplified modes for efficient training and realistic modes for accurate scenario generation, allowing the same framework to adapt to different needs without sacrificing either efficiency or accuracy
Solution Approach 2:
The patent changes environmental parameters (complexity level, realism, detail) to optimize the balance between training efficiency and simulation accuracy. By adjusting these parameters, the system can generate realistic attack scenarios while maintaining training effectiveness
2Measurement precision
If expert knowledge is used to generate attack scenarios, then scenario accuracy is improved, but scenario diversity and adaptability deteriorate
Solution Approach 1:
The cyberattack agent performs autonomous penetration testing by independently analyzing target systems, identifying vulnerabilities, and generating attack scenarios without human intervention. This self-service capability enables unlimited scenario diversity while maintaining high accuracy through the agent's learned expertise
Solution Approach 2:
The patent replaces manual expert knowledge with an AI-based cyberattack agent that uses reinforcement learning and natural language processing to generate scenarios. This substitution eliminates human limitations in scenario diversity while maintaining accuracy through the agent's trained understanding of attack methodologies
3Adaptability or versatility
If random warfare scenarios are generated for training, then scenario variety is improved, but training effectiveness and realism deteriorate
Solution Approach 1:
The system performs preliminary analysis of the target system to identify actual vulnerabilities and attack vectors before generating training scenarios. This preliminary action ensures that randomly generated scenarios are replaced with targeted, realistic scenarios that accurately reflect potential attack paths, thereby maintaining training effectiveness while preserving scenario variety
Data Source
AI summary
Disclosed herein is a method for generating a cyberattack sequence based on reinforcement learning. The method includes generating a cyberattack simulation environment, training a cyberattack agent model based on the cyberattack simulation environment, and generating an attack sequence using the trained cyberattack agent model.