Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR). This work extends previous efforts on developing RL methods for path analysis within enterprise networ...
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| creator | Huang, Lanxiao Cody, Tyler Redino, Christopher Rahman, Abdul Kakkar, Akshay Kushwaha, Deepak Wang, Cheng Clark, Ryan Radke, Daniel Beling, Peter Bowen, Edward |
| description | Reinforcement learning (RL) operating on attack graphs leveraging cyber
terrain principles are used to develop reward and state associated with
determination of surveillance detection routes (SDR). This work extends
previous efforts on developing RL methods for path analysis within enterprise
networks. This work focuses on building SDR where the routes focus on exploring
the network services while trying to evade risk. RL is utilized to support the
development of these routes by building a reward mechanism that would help in
realization of these paths. The RL algorithm is modified to have a novel
warm-up phase which decides in the initial exploration which areas of the
network are safe to explore based on the rewards and penalty scale factor. |
| doi_str_mv | 10.48550/arxiv.2211.03027 |
| format | Article |
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terrain principles are used to develop reward and state associated with
determination of surveillance detection routes (SDR). This work extends
previous efforts on developing RL methods for path analysis within enterprise
networks. This work focuses on building SDR where the routes focus on exploring
the network services while trying to evade risk. RL is utilized to support the
development of these routes by building a reward mechanism that would help in
realization of these paths. The RL algorithm is modified to have a novel
warm-up phase which decides in the initial exploration which areas of the
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terrain principles are used to develop reward and state associated with
determination of surveillance detection routes (SDR). This work extends
previous efforts on developing RL methods for path analysis within enterprise
networks. This work focuses on building SDR where the routes focus on exploring
the network services while trying to evade risk. RL is utilized to support the
development of these routes by building a reward mechanism that would help in
realization of these paths. The RL algorithm is modified to have a novel
warm-up phase which decides in the initial exploration which areas of the
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terrain principles are used to develop reward and state associated with
determination of surveillance detection routes (SDR). This work extends
previous efforts on developing RL methods for path analysis within enterprise
networks. This work focuses on building SDR where the routes focus on exploring
the network services while trying to evade risk. RL is utilized to support the
development of these routes by building a reward mechanism that would help in
realization of these paths. The RL algorithm is modified to have a novel
warm-up phase which decides in the initial exploration which areas of the
network are safe to explore based on the rewards and penalty scale factor.</abstract><doi>10.48550/arxiv.2211.03027</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Cryptography and Security Computer Science - Learning Computer Science - Networking and Internet Architecture |
| title | Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain |
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