APRIL: towards Scalable and Transferable Autonomous Penetration Testing in Large Action Space via Action Embedding

Penetration testing (pentesting) assesses cybersecurity through simulated attacks, while the conventional manual-based method is costly, time-consuming, and personnelconstrained. Reinforcement learning (RL) provides an agentenvironment interaction learning paradigm, making it a promising way for aut...

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Veröffentlicht in:	IEEE transactions on dependable and secure computing 2024-12, p.1-17
Hauptverfasser:	Zhou, Shicheng, Liu, Jingju, Lu, Yuliang, Yang, Jiahai, Hou, Dongdong, Zhang, Yue, Hu, Shulong
Format:	Artikel
Sprache:	eng
Schlagworte:	action embedding Cyberspace large action space Manuals Network security Penetration testing Planning policy transfer Reinforcement learning Scalability Semantics Testing Training
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