CR-Honeynet: A Cognitive Radio Learning and Decoy-Based Sustenance Mechanism to Avoid Intelligent Jammer
Suman Bhunia, Edward L. Miles, Shamik Sengupta, Felisa J. Vázquez-Abad · IEEE Transactions on Cognitive Communications and Networking · 2018
Cognitive radio network (CRN) enables secondary users to borrow unused spectrum from the proprietary users in a dynamic and opportunistic manner. However, dynamic and open access nature of available spectrum brings a severe sustenance challenge amongst CRNs which makes them vulnerable to various spectrum etiquette attacks. Jamming-based denial-of-service attack poses serious threats to legitimate communications and packet delivery. A rational attacker targets specific transmission characteristics to find the highest impacting connection of CRN and causes maximum disruption. With the help of software-defined radios, we have shown that an attacker can intelligently target a particular communication. In this paper, inspired by the honeypot concept in cybercrime, we propose a honeynet-based defense mechanism, which aims to deter the attacker from jamming legitimate communications. The honeynet passively learns the attacker's strategy from the history of attacks and actively adapts preemptive decoy mechanisms to prevent attacks on legitimate communications. Simulation results show that the with the help of honeynet mechanism, CRN successfully avoids jamming attacks and thereby improves system performance regarding packet delivery ratio. We further built a prototype using off-the-shelf software defined radio that proves the effectiveness of the proposed mechanism.