Assessing generalizability of Deep Reinforcement Learning algorithms for Automated Vulnerability Assessment and Penetration Testing

Assessing generalizability of Deep Reinforcement Learning algorithms for Automated Vulnerability Assessment and Penetration Testing

Modern cybersecurity best practices and standards require continuous Vulnerability Assessment (VA) and Penetration Test (PT). These activities are human- and time-expensive. The research community is trying to propose autonomous or semi-autonomous solutions based on Deep Reinforcement Learning (DRL)...

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Bibliographic Details
Main Authors: Andrea Venturi, Mauro Andreolini, Mirco Marchetti, Michele Colajanni
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Array
Subjects:
Vulnerability assessment
Penetration testing
Deep reinforcement learning
Deep Q-network
Adversarial actor–critic
Proximal policy optimization
Online Access:http://www.sciencedirect.com/science/article/pii/S2590005624000316
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