Behaviour-diverse automatic penetration testing: a coverage-based deep reinforcement learning approach

Reinforcement Learning (RL) is gaining importance in automating penetration testing as it reduces human effort and increases reliability. Nonetheless, given the rapidly expanding scale of modern network infrastructure, the limited testing scale and monotonous strategies of existing RL-based automate...

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Veröffentlicht in:	Frontiers of Computer Science 2025-03, Vol.19 (3), p.193309, Article 193309
Hauptverfasser:	Yang, Yizhou, Chen, Longde, Liu, Sha, Wang, Lanning, Fu, Haohuan, Liu, Xin, Chen, Zuoning
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Architecture Automation Behavior Chebyshev approximation Computer Science Decision making Deep learning Efficiency Markov analysis Methods Network reliability Neural networks Research Article
Online-Zugang:	Volltext
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