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Prospective Artificial Intelligence Approaches for Active Cyber Defence

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arxiv 2104.09981 v1 pith:UCE2H675 submitted 2021-04-20 cs.CR cs.LG

classification cs.CRcs.LG
keywords cyberdefenceactiveapproachesartificialclassesenableintelligence
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Cybercriminals are rapidly developing new malicious tools that leverage artificial intelligence (AI) to enable new classes of adaptive and stealthy attacks. New defensive methods need to be developed to counter these threats. Some cybersecurity professionals are speculating AI will enable corresponding new classes of active cyber defence measures -- is this realistic, or currently mostly hype? The Alan Turing Institute, with expert guidance from the UK National Cyber Security Centre and Defence Science Technology Laboratory, published a research roadmap for AI for ACD last year. This position paper updates the roadmap for two of the most promising AI approaches -- reinforcement learning and causal inference - and describes why they could help tip the balance back towards defenders.

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  1. General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers

    cs.CR 2025-06 conditional novelty 5.0 of 10

    A graph-neural-network plus optimal-transport agent trained on procedurally generated networks generalizes across topology changes and two attacker types in the CAGE 2 simulation.

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