Performance Analysis of Secondary Users in the Presence of Attackers in Cognitive Radio Networks

Cognitive radio network is regarded as an emerging technology to solve 'spectrum scarcity' through dynamic spectrum access to support exponentially increasing wireless subscriptions. However, spectrum sensing and dynamic spectrum sharing in cognitive radio network invite more security attacks making security as one of the main concerns. In this paper, we analyze the performance of the secondary users in terms of physical-layer security in the presence of both eavesdroppers and jammers in cognitive radio networks. In this case, secondary users not only have to compete against eavesdroppers and jammers (who are trying to reduce the secrecy rates of secondary users) but also have to compete with other secondary users to gain access to idle channels to gain high secrecy rates. The main contribution of this work is to investigate game theoretical model to maximize utility of secondary users in the presence of eavesdroppers and jammers. The proposed approach can be particularized to a scenario with eavesdroppers only or jammers only while evaluating the performance of secondary user physical layer security. Performance of the proposed approach is evaluated with the help of numerical results obtained from simulations and the proposed approach outperforms other existing methods. Furthermore, there is sever impact on utilities (secrecy rates) of secondary users when both eavesdroppers and jammers are active in the network.

[1]  Günes Karabulut-Kurt,et al.  Physical layer security in cognitive radio networks: A beamforming approach , 2013, 2013 First International Black Sea Conference on Communications and Networking (BlackSeaCom).

[2]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[3]  Tamer Başar,et al.  A game-theoretic view on the physical layer security of cognitive radio networks , 2013, 2013 IEEE International Conference on Communications (ICC).

[4]  Hadi Shahriar Shahhoseini,et al.  An analysis on interactions among secondary user and unknown jammer in cognitive radio systems by fictitious play , 2013, 2013 10th International ISC Conference on Information Security and Cryptology (ISCISC).

[5]  Danda B. Rawat,et al.  Advances on Security Threats and Countermeasures for Cognitive Radio Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[6]  Hesham El Gamal,et al.  On the Secrecy Capacity of Fading Channels , 2007, ISIT.

[7]  Jack L. Burbank,et al.  Security in Cognitive Radio Networks: The Required Evolution in Approaches to Wireless Network Security , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[8]  Gongjun Yan,et al.  Game Theory for Resource Allocation in Wireless Networks , 2010 .

[9]  Jaime Lloret Mauri,et al.  Cognitive Networks: Applications and Deployments , 2014 .

[10]  Husheng Li,et al.  Cognitive Radio Network , 2012 .

[11]  Matthew R. McKay,et al.  Secure Transmission With Artificial Noise Over Fading Channels: Achievable Rate and Optimal Power Allocation , 2010, IEEE Transactions on Vehicular Technology.

[12]  Sachin Shetty,et al.  Dynamic Spectrum Access for Wireless Networks , 2015, SpringerBriefs in Electrical and Computer Engineering.

[13]  Zhu Han,et al.  Secure wireless communications via cooperation , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[14]  Chunsheng Xin,et al.  A density based scheme to countermeasure spectrum sensing data falsification attacks in cognitive radio networks , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[15]  Richard E. Blahut,et al.  Secrecy capacity of SIMO and slow fading channels , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[16]  Philip J. Reny,et al.  Non-Cooperative Games : Equilibrium Existence , 2005 .

[17]  Chunsheng Xin,et al.  Detection of PUE Attacks in Cognitive Radio Networks Based on Signal Activity Pattern , 2014, IEEE Transactions on Mobile Computing.

[18]  Xi Fang,et al.  Coping with a Smart Jammer in Wireless Networks: A Stackelberg Game Approach , 2013, IEEE Transactions on Wireless Communications.

[19]  R. Negi,et al.  Secret communication in presence of colluding eavesdroppers , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[20]  Symeon Chatzinotas,et al.  Power allocation for energy-constrained cognitive radios in the presence of an eavesdropper , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[21]  Hirosuke Yamamoto,et al.  A coding theorem for secret sharing communication systems with two Gaussian wiretap channels , 1991, IEEE Trans. Inf. Theory.

[22]  Shuangqing Wei,et al.  Cross-layer detection of stealthy jammers in multihop cognitive radio networks , 2013, 2013 International Conference on Computing, Networking and Communications (ICNC).

[23]  A. U.S.,et al.  Computation of Equilibria in Noncooperative Games , 2005 .

[24]  Nan Liu,et al.  Towards the Secrecy Capacity of the Gaussian MIMO Wire-Tap Channel: The 2-2-1 Channel , 2007, IEEE Transactions on Information Theory.

[25]  Y. Thomas Hou,et al.  Cognitive radio communications and networks: principles and practice , 2012 .

[26]  Xiaohua Li,et al.  Jamming probabilities and throughput of cognitive radio communications against a wideband jammer , 2013, 2013 47th Annual Conference on Information Sciences and Systems (CISS).

[27]  John H. Reif,et al.  Computation of equilibriain noncooperative games , 2005 .

[28]  John C. Harsanyi,et al.  Общая теория выбора равновесия в играх / A General Theory of Equilibrium Selection in Games , 1989 .

[29]  Shlomo Shamai,et al.  Secure Communication Over Fading Channels , 2007, IEEE Transactions on Information Theory.

[30]  S. Alrabaee,et al.  Game Theory for Security in Cognitive Radio Networks , 2012, 2012 International Conference on Advances in Mobile Network, Communication and Its Applications.