AlphaSOC: Reinforcement Learning-based Cybersecurity Automation for Cyber-Physical Systems | |
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Author | |
Abstract |
Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordi-nate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces. |
Year of Publication |
2022
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Conference Name |
2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)
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