Invited Speakers

Dr. Jimson Mathew (IIT Patna, India)

Prof. Ashutosh Kumar Singh (NIT Kurukshetra, India)

Prof. Kok-Lim Alvin Yau (Sunway University, Malaysia)
Artificial Intelligence-based Countermeasure and Attack Agents: Who win?
Reinforcement learning (RL) is a branch of artificial intelligence that has been well investigated to show system performance enhancement. Yet, the investigation of the security aspects of RL is at its infancy. This paper offers a thorough investigation of the security aspect of RL. In this paper, RL is applied to cluster size adjustment in clustering for distributed cognitive radio networks whereby the unlicensed or secondary users (SUs) explore and use underutilized channels (or white spaces) owned by the licensed or primary users. Clustering segregates SUs in a network into logical groups; and each cluster consists of a leader (or a clusterhead) and member nodes. The investigation covers the effects of an important RL parameter, specifically the learning rate, in a dynamic operating environment. Both clusterhead and member node leverage on RL: a) the clusterhead uses RL to countermeasure attacks, and b) the SU uses RL to launch attacks with various attack probabilities. Simulation results have shown that a RL model with the right learning rate for clusterhead provides the highest network scalability when being attacked with various attack probabilities and different learning rates in a dynamic operating environment.
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