Defending against Byzantine attack in cooperative spectrum sensing relying on a reliable reference

This paper considers countermeasures against Byzantine attack, also known as spectrum sensing data falsification (SSDF) attack, which poses huge threats on the reliability of cooperative spectrum sensing (CSS). Due to lack of the ground-truth spectrum state, a reliable defense reference is vital to identify malicious behaviors and perform effective data fusion. Motivated by the fact that the existing references have strong assumptions such as the attackers are in minority or a trust node exists for data fusion, this paper proposes a novel defense reference which jointly exploits the cognitive process of spectrum sensing and spectrum access in a closed-loop manner, to provide the defense scheme with a solid basis. Then, based on the proposed reference, we design an optimal cooperative spectrum sensing scheme. Furthermore, numerical simulations verify the proposed scheme's favorable performance, even in critical cases that malicious sensors are in majority.

[1]  Zhu Han,et al.  Byzantine Attack and Defense in Cognitive Radio Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[2]  P.K. Varshney,et al.  Optimal Data Fusion in Multiple Sensor Detection Systems , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[4]  Yuping Zhao,et al.  A New Cooperative Detection Technique with Malicious User Suppression , 2009, 2009 IEEE International Conference on Communications.

[5]  Fabrizio Granelli,et al.  Identification and Punishment Policies for Spectrum Sensing Data Falsification Attackers Using Delivery-Based Assessment , 2016, IEEE Transactions on Vehicular Technology.

[6]  Qihui Wu,et al.  Performance analysis of probabilistic soft SSDF attack in cooperative spectrum sensing , 2014, EURASIP J. Adv. Signal Process..

[7]  Zhu Han,et al.  Catch Me if You Can: An Abnormality Detection Approach for Collaborative Spectrum Sensing in Cognitive Radio Networks , 2010, IEEE Transactions on Wireless Communications.

[8]  Yunghsiang Sam Han,et al.  Distributed Detection in Tree Topologies With Byzantines , 2013, IEEE Transactions on Signal Processing.

[9]  Ian F. Akyildiz,et al.  Cooperative spectrum sensing in cognitive radio networks: A survey , 2011, Phys. Commun..

[10]  Yunghsiang Sam Han,et al.  Distributed Detection in Tree Networks: Byzantines and Mitigation Techniques , 2014, IEEE Transactions on Information Forensics and Security.

[11]  Qun Li,et al.  Defending Against Cooperative Attacks in Cooperative Spectrum Sensing , 2013, IEEE Transactions on Wireless Communications.

[12]  Danijela Cabric,et al.  Reputation-based cooperative spectrum sensing with trusted nodes assistance , 2010, IEEE Communications Letters.

[13]  Qihui Wu,et al.  Robust Spectrum Sensing With Crowd Sensors , 2014, IEEE Trans. Commun..

[14]  K. B. Letaief,et al.  Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks , 2009, IEEE Transactions on Wireless Communications.

[15]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[16]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[17]  Majid Khabbazian,et al.  Malicious User Detection in a Cognitive Radio Cooperative Sensing System , 2010, IEEE Transactions on Wireless Communications.

[18]  Peng Ning,et al.  A Byzantine Attack Defender in Cognitive Radio Networks: The Conditional Frequency Check , 2013, IEEE Transactions on Wireless Communications.

[19]  Do-Hoon Kim,et al.  Selfish attacks and detection in cognitive radio Ad-Hoc networks , 2013, IEEE Network.

[20]  Kaigui Bian,et al.  Robust Distributed Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[21]  Yunghsiang Sam Han,et al.  Distributed Bayesian Detection in the Presence of Byzantine Data , 2013, IEEE Transactions on Signal Processing.

[22]  Peng Ning,et al.  HMM-Based Malicious User Detection for Robust Collaborative Spectrum Sensing , 2013, IEEE Journal on Selected Areas in Communications.

[23]  Pramod K. Varshney,et al.  Collaborative Spectrum Sensing in the Presence of Byzantine Attacks in Cognitive Radio Networks , 2011, IEEE Trans. Signal Process..

[24]  Qihui Wu,et al.  Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions , 2013, IEEE Signal Processing Magazine.

[25]  H. Urkowitz.  Energy detection of unknown deterministic signals , 1967 .

[26]  Pramod K. Varshney,et al.  Distributed Inference with Byzantine Data: State-of-the-Art Review on Data Falsification Attacks , 2013, IEEE Signal Processing Magazine.