Reinforcement Learning Attack Scenario Generation
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Solution Overview
Problem
Current techniques for generating attack scenarios require manual creation of basic scenarios and are not adaptable to real environments, leading to inefficiencies and inconsistencies between simulation and actual systems, which increases man-hours and reduces effectiveness.
Innovation Solution
An attack scenario simulation device that uses reinforcement learning to automatically generate scenarios by simulating actions, tactics, and rewards, allowing adaptation to real environments without pre-defined basic scenarios, and updates success probabilities based on execution results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual creation of basic attack scenarios is performed, then attack scenarios can be generated, but it requires a lot of man-hours and experts familiar with attack tools
Solution Approach 1:
The system enables automatic generation of attack scenarios through reinforcement learning, where the attack scenario generation unit autonomously creates scenarios without requiring manual intervention from experts. The system learns and generates scenarios independently by evaluating multiple candidate scenarios and selecting optimal ones based on simulated attack outcomes.
Solution Approach 2:
The patent replaces the manual mechanical process of expert-created scenario development with an automated computational system. Reinforcement learning algorithms automatically generate and evaluate attack scenarios, substituting human experts' manual work with machine-based automated scenario generation that does not require specialized knowledge of attack tools.
2Adaptability or versatility
If basic attack scenarios are defined in advance, then attack scenarios can be generated for multiple systems, but new attack methods cannot be adapted without defining new basic scenarios
Solution Approach 1:
The system dynamically generates attack scenarios based on current system configurations and threat landscapes rather than relying on static pre-defined scenarios. The reinforcement learning approach allows the system to adapt to new attack methods automatically by learning from simulated attacks and updating its scenario generation capabilities without requiring manual redefinition of basic scenarios.
Solution Approach 2:
The system incorporates feedback loops where attack scenarios are simulated, evaluated, and used to improve future scenario generation. The reinforcement learning mechanism learns from the outcomes of simulated attacks and adjusts its scenario generation strategy, enabling automatic adaptation to new attack methods without manual intervention.
3Extent of automation
If attack scenarios are generated on simulation, then automation is achieved, but inconsistencies between simulation environment and actual machine reduce applicability to real environment
Solution Approach 1:
The system performs preliminary simulation of attack scenarios in a controlled environment before deployment. By evaluating multiple candidate scenarios through simulation and selecting the most effective ones in advance, the system prepares reliable attack scenarios that can be confidently applied to real environments. The simulation phase acts as a preliminary testing ground to ensure scenario effectiveness.
Solution Approach 2:
The system creates simplified copies of real system configurations in the simulation environment, allowing attack scenarios to be tested and validated before real-world application. The simulation environment replicates key characteristics of target systems, enabling realistic scenario testing without direct exposure to actual systems, thereby ensuring reliability when scenarios are deployed.
Data Source
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AI summary
Attack scenario information describes each state of an information processing system to be attacked and an attack scenario including a chain of actions that can be taken in the state, an action that transitions from a first state to a second state is obtained with reference to state information, action information, and attack tactics information, a reward of the action is obtained with reference to reward information, the action information, and the attack tactics information, an expected reward of the reward of the action that transitions from the first state to the second state is obtained with reference to success probability information, the highest expected reward is set as a state value of reinforcement learning of the first state among the expected rewards of the action, and the attack scenario is generated by the reinforcement learning.