DynPen: Automated Penetration Testing in Dynamic Network Scenarios Using Deep Reinforcement Learning
Abstract: Penetration testing, a crucial industrial practice for securing networked systems and infrastructures, has traditionally depended on the extensive expertise of human professionals.
Abstract: In this article, we study a class of two-player deterministic finite-horizon difference games with coupled inequality constraints, where each player has two types of decision variables: one ...
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