Deep Learning-Based Automated Attacker Models: A Hierarchical Attacker-Defender Framework under Partial Observability

This work examines how AI- and deep learning-enabled cyberattacks can be carried out and investigates how adaptive defence strategies can be developed to counter them. A conceptual model of an autonomous attacker is proposed and formalized within a hierarchical attacker-defender interaction framework. Based on this formulation, adaptive defence mechanisms and a theoretical evaluation framework are defined to assess the effectiveness of learning-based cyber offence and defence.

Keywords

Cybersecurity, Adversarial AI, Reinforcement Learning, Markov Games, Autonomous Agents, Adaptive Defence, POSG, H-POSG, H-MARL

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