CR-Honeynet: A learning & decoy based Sustenance Mechanism Against Jamming Attack in CRN

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 serious challenge of sustenance amongst CRNs which makes them vulnerable to various spectrum etiquette attacks. Jamming-based denial of service (DoS) attack poses serious threats to legitimate communications and packet delivery. A rational attacker targets certain transmission characteristics to find the highest impacting communication of CRN and causes maximum disruption. 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 past history of attacks and actively adapts preemptive decoy mechanisms to prevent attacks on legitimate communications. Simulation results show that the with help of honeynet mechanism, CRN successfully avoids jamming attacks and thereby improves system performance in terms of packet delivery ratio. Keywords—Cognitive Radio, Jamming, Honeynet, Stochastic Learning