VIRUS COUNTERMEASURES IN COGNITIVE WIRELESS NETWORKS BASED ON REINFORCEMENT LEARNING

Cognitive wireless networks improve the utilization of the radio spectrum by allowing secondary users (SU) to access licensed bands owned by primary users. Cognitive wireless network has the possibility that viruses invade primary users and occupy the licensed frequency band to reduce the traffic of secondary users. This paper proposes a cognitive wireless network virus countermeasure strategy based on multi-agent reinforcement learning, which minimizes the impact of virus attack on SUs by optimizing the dynamic routing strategy of SUs.