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Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks. (arXiv:2308.04909v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
This paper focuses on the impact of leveraging autonomous offensive
approaches in Deep Reinforcement Learning (DRL) to train more robust agents by
exploring the impact of applying adversarial learning to DRL for autonomous
security in Software Defined Networks (SDN). Two algorithms, Double Deep
Q-Networks (DDQN) and Neural Episodic Control to Deep Q-Network (NEC2DQN or
N2D), are compared. NEC2DQN was proposed in 2018 and is a new member of the
deep q-network (DQN) family of algorithms. The attacker has full observability …
adversarial algorithms autonomous cyber cyber security defined impact networks offensive sdn security software train