Mitigating jamming attacks in mobile cognitive networks through time hopping

5G wireless networks will support massive connectivity mainly due to device-to-device communications. An enabling technology for device-to-device links is the dynamical spectrum access. The devices, which are equipped with cognitive radios, are to be allowed to reuse spectrum occupied by cellular links. The dynamical spectrum availability makes cognitive users switch between channels. Switching leads to energy consumption, latency, and communication overhead in general. The performance degrades even more when the network is under jamming attack. This type of attack is one of the most detrimental attacks. Addressing jamming while maintaining a desired quality of service is a challenge. While existing anti-jamming mechanisms assume stationary users, in this paper, we propose and evaluate countermeasures for mobile cognitive users. We propose two time-based techniques, which, unlike other existing frequency-based techniques, do not assume accessibility to multiple channels and hence do not rely on switching to countermeasure jamming. We achieve analytical solutions of jamming, switching, and error probabilities. Based on our findings, the proposed techniques out perform other existing frequency-based techniques. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Willi Meier,et al.  SHA-3 proposal BLAKE , 2009 .

[2]  Hai Su,et al.  Jamming-Resilient Dynamic Spectrum Access for Cognitive Radio Networks , 2011, 2011 IEEE International Conference on Communications (ICC).

[3]  Ekram Hossain,et al.  Evolution toward 5G multi-tier cellular wireless networks: An interference management perspective , 2014, IEEE Wireless Communications.

[4]  Keith Q. T. Zhang,et al.  A general analytical approach to multi-branch selection combining over various spatially correlated fading channels , 2002, IEEE Trans. Commun..

[5]  Antonio Petrolino,et al.  A Mobile-to-Mobile Fading Channel Simulator Based on an Orthogonal Expansion , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[6]  Aggelos Bletsas,et al.  A simple Cooperative diversity method based on network path selection , 2005, IEEE Journal on Selected Areas in Communications.

[7]  Xiaohua Li,et al.  Anti-jamming performance of cognitive radio networks , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[8]  Liang Xiao,et al.  Anti-Jamming Transmission Stackelberg Game With Observation Errors , 2015, IEEE Communications Letters.

[9]  Bechir Hamdaoui,et al.  The impact of stochastic resource availability on cognitive network performance: modeling and analysis , 2016, Wirel. Commun. Mob. Comput..

[10]  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.

[11]  Wen-Long Chin,et al.  Channel-Based Detection of Primary User Emulation Attacks in Cognitive Radios , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[12]  Simone Campanoni.  Competition , 1866, Nature.

[13]  P. Bello,et al.  Correction to "The Influence of Fading Spectrum on the Binary Error Probabilities of Incoherent and Differentially Coherent Matched Filter Receivers" , 1963 .

[14]  Srdjan Capkun,et al.  Jamming-resistant Key Establishment using Uncoordinated Frequency Hopping , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[15]  Ye Li,et al.  Anti-jamming property of clustered OFDM for dispersive channels , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[16]  Ashraf Al Daoud,et al.  Secondary Pricing of Spectrum in Cellular CDMA Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[17]  Wenliang Du,et al.  A physical layer authentication scheme for countering primary user emulation attack , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[18]  B. Nelin,et al.  Corrections to "The Effect of Frequency Selective Fading on the Binary Error Probabilities of Incoherent and Differentially Coherent Matched Filter Receivers" , 1963 .

[19]  H. Vincent Poor,et al.  Prospect theoretic analysis of anti-jamming communications in cognitive radio networks , 2014, 2014 IEEE Global Communications Conference.

[20]  Chunsheng Xin,et al.  A game-theoretical anti-jamming scheme for cognitive radio networks , 2013, IEEE Network.

[21]  Alejandro Betancourt,et al.  A fictitious play-based game-theoretical approach to alleviating jamming attacks for cognitive radios , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Attila A. Yavuz,et al.  Pseudorandom Time-Hopping Anti-Jamming Technique for Mobile Cognitive Users , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

[23]  A. S. Akki.  Statistical properties of mobile-to-mobile land communication channels , 1994 .

[24]  Mohamed Grissa,et al.  LPOS: Location Privacy for Optimal Sensing in Cognitive Radio Networks , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[25]  Weidong Xiang,et al.  Detecting multi-channel wireless microphone user emulation attacks in white space with noise , 2013, 8th International Conference on Cognitive Radio Oriented Wireless Networks.

[26]  Bechir Hamdaoui,et al.  Delay performance modeling and analysis in clustered cognitive radio networks , 2014, 2014 IEEE Global Communications Conference.

[27]  F. Haber,et al.  A statistical model of mobile-to-mobile land communication channel , 1986, IEEE Transactions on Vehicular Technology.

[28]  K. J. Ray Liu,et al.  Anti-Jamming Games in Multi-Channel Cognitive Radio Networks , 2012, IEEE Journal on Selected Areas in Communications.

[29]  Shuai Li,et al.  Security and privacy of collaborative spectrum sensing in cognitive radio networks , 2012, IEEE Wireless Communications.