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

TL;DR

This paper proposes a dynamic, learning-based approach using Markov Decision Processes to optimize countermeasures against Lion attacks in cognitive radio systems, enhancing security by intelligently managing frequency handoffs.

Contribution

It introduces a novel MDP-based method for dynamically freezing windows and performing frequency handoffs to mitigate Lion attacks in cognitive radio networks.

Findings

The proposed method effectively reduces the impact of Lion attacks.

It enables secondary users to select optimal strategies across layers.

Simulation results demonstrate improved resilience against multi-layer attacks.

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, PUE and Lion attack. Lion attack is multi layer attacks that has effected on two layers. The Lion attack uses PUE or jamming attack in physical layer to disrupt TCP protocol 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 windows size of TCP would be decrease because of a wrong decision caused by unawareness. To Mitigate the Lion attack the cross layer design is usually used to freeze its windows 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.