Problem Statement and Motivation

Cognitive Radio Network (CRN) has to stall its packet transmission periodically to sense the spectrum for Primary User’s (PU’s) transmission. The limited and dynamically available spectrum and fixed periodic schedule of transmission interruption make it harder to model the performance of a CRNs. Again, an open and dynamic spectrum access model brings forth a serious challenge of sustenance among the CRN and makes them more susceptible to jamming-based denial of service (DoS) attacks. Inspired by honeypot in the network security, we propose a honeynet based defense mechanism called CR-honeynet. CR-honeynet aims to avoid attacks on legitimate communications by dedicating a Secondary User (SU) as a honeynode, to deter the attacker from attacking legitimate SUs and attack the honeynode instead. CR-honeynet passively learns the attacker’s strategy from the history of attacks and actively adapts preemptive decoy mechanisms to prevent attacks on legitimate communications. Agian, dedicating an SU as honeynode, on account of its permanent idleness, is wasteful of an entire node as a resource. We seek to resolve the dilemma by dynamically selecting the honeynode for each transmission period. The contribution of the current paper is two-fold. Initially, we develop the first comprehensive queuing model for CRNs, which pose unique modeling challenges, due to their fixed periodic sensing and transmission cycles. In the second step, we introduce a series of strategies for honeynode selection to combat these attacks while keeping the CRN’s performance optimal for different traffic scenarios.



The protype is built using USRP 210 software defined radios, controlled using GNURadio interfaces.


  1. S Bhunia, S Sengupta and F Vazquez-Abad, “Performance Analysis of CR-Honeynet to prevent Jamming Attack through Stochastic Modeling”, Elsevier Pervasive and Mobile Computing, Volume 21, August 2015, Pages 133–149. DOI:10.1016/j.pmcj.2015.04.004.

  2. S Bhunia, S Sengupta and F Vazquez-Abad, “CR-Honeynet: A learning & decoy based Sustenance Mechanism Against Jamming Attack in CRN”, proc. of IEEE MILCOM 2014, Baltimore, doi:10.1109/MILCOM.2014.197.

  3. S Bhunia, X Su, S Sengupta and F Vazquez-Abad, “Stochastic model for Cognitive Radio Networks under jamming attacks and honeypot-based prevention”, 15th International Conference on Distributed Computing and Networking (ICDCN), 2014.

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