AI policies learn cybersecurity penetration testing faster with history aggregation

Researchers have developed and evaluated reinforcement learning policies for penetration testing in cybersecurity scenarios with partial observability. They compared several Proximal Policy Optimization (PPO) variants, including those using LSTMs and TrXL architectures, against a baseline PPO approach. The study found that history aggregation significantly improved policy convergence, achieving up to four times faster results than other methods, and provided insights into the learned policies. AI

IMPACT This research could lead to more robust and automated cybersecurity tools by improving AI's ability to handle complex, partially observable environments.

RANK_REASON Academic paper detailing a novel application of RL to cybersecurity with empirical evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• DagsHub
• Hugging Face
• LSTM
• Markov decision processes
• Partially Observable MDPs
• Proximal Policy Optimization

• paper
• other

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