Security aspects in the cognition cycle of distributed cognitive radio networks: a survey from a multi-agent perspective
Cognitive Radio (CR) enables Secondary Users (SUs) to observe, learn and take the right action on its operating environment at any time instant. This feature has inevitably exposed the SUs to artificial intelligence attacks from adversaries. This paper provides a survey on various kinds of attacks, as well as reviews and proposes mitigation approaches to tackle them from a multi-agent perspective. Additionally, this paper shows the impact and criticality of artificial intelligence attacks on network-wide performance. We aim to establish a foundation, and to spark new research interests in this new and critical area.
cognition cycle cognitive radio cr artificial intelligence attacks cc security attacks vulnerabilities threat mitigation malicious jamming pue primary user emulation applied artificial intelligence payoff propagation rl reinforcement learning