Defense against Lion Attack in Cognitive Radio Systems using the Markov Decision Process Approach

Abstract

Abstract Cognitive radio (CR) technology is a solution to solve the lack of spectrum by allowing the secondary user to use licensed bands. There are several potential security challenges for cognitive radio like jamming, primary user emulation (PUE) and lion attack. The lion attack is a multi-layer attack that has effects on two layers. It uses PUE or jamming attack in physical layer to disrupt transmission control protocol (TCP) in transport layer. Since transport layer is unaware of physical layer situation, when it occurs to an unacknowledged packet, there is no way to distinguish between congestion and disconnection in the physical layer. So the window size of TCP would be decreased because of a wrong decision caused by unawareness. To mitigate the lion attack the cross layer design is usually used to freeze its window size during frequency handoff. The main issue in this solution is finding the best strategy for freezing. In this paper, we propose a dynamic method for freezing and performing frequency handoff to counter lion attack. Also a learning model based on Markov decision process (MDP) is applied. By using the proposed method, the secondary user can choose an optimal strategy in both physical and transport layers to reduce the effect of lion attack.