Dogfight in Spectrum: Combating Primary User Emulation Attacks in Cognitive Radio Systems—Part II: Unknown Channel Statistics

The defense against the Primary User Emulation Attack (PUE) is studied in the scenario of unknown channel statistics (coined blind dogfight in spectrum). The algorithm of the adversarial bandit problem is adapted to the context of blind dogfight. Both cases of complete and partial information about the rewards of different channels are analyzed. Performance bounds are obtained subject to arbitrary channel statistics and attack policy. Several attack strategies, namely uniformly random, selectively random and maximal interception attacks, are discussed. The validity of the defense strategy is then demonstrated by numerical simulation results.

[1]  Husheng Li,et al.  Multiagent -Learning for Aloha-Like Spectrum Access in Cognitive Radio Systems , 2010 .

[2]  Edward J. Sondik,et al.  The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..

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

[4]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.

[5]  Ryan W. Thomas,et al.  A Bayesian Game Analysis of Emulation Attacks in Dynamic Spectrum Access Networks , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[6]  Santhanakrishnan Anand,et al.  Mitigating primary user emulation attacks in dynamic spectrum access networks using hypothesis testing , 2009, MOCO.

[7]  Zhu Han,et al.  Blind Dogfight in Spectrum: Combating Primary User Emulation Attacks in Cognitive Radio Systems with Unknown Channel Statistics , 2010, 2010 IEEE International Conference on Communications.

[8]  Roger B. Myerson,et al.  Game theory - Analysis of Conflict , 1991 .

[9]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[10]  Husheng Li,et al.  Multi-Agent Q-Learning for Competitive Spectrum Access in Cognitive Radio Systems , 2010, 2010 Fifth IEEE Workshop on Networking Technologies for Software Defined Radio Networks (SDR).

[11]  Peter Auer,et al.  The Nonstochastic Multiarmed Bandit Problem , 2002, SIAM J. Comput..

[12]  Nicolas Vieille,et al.  The MaxMin value of stochastic games with imperfect monitoring , 2003, Int. J. Game Theory.

[13]  Zhu Han,et al.  Dogfight in Spectrum: Combating Primary User Emulation Attacks in Cognitive Radio Systems, Part I: Known Channel Statistics , 2010, IEEE Transactions on Wireless Communications.

[14]  Hai Jiang,et al.  Medium access in cognitive radio networks: A competitive multi-armed bandit framework , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[15]  H. Kushner,et al.  Stochastic Approximation and Recursive Algorithms and Applications , 2003 .

[16]  Santhanakrishnan Anand,et al.  Detecting Primary User Emulation Attacks in Dynamic Spectrum Access Networks , 2009, 2009 IEEE International Conference on Communications.

[17]  Husheng Li,et al.  Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: A two by two case , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[18]  J. Mitola,et al.  Cognitive radio for flexible mobile multimedia communications , 1999, 1999 IEEE International Workshop on Mobile Multimedia Communications (MoMuC'99) (Cat. No.99EX384).

[19]  Kaigui Bian,et al.  Security vulnerabilities in IEEE 802.22 , 2008, WICON 2008.

[20]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[21]  Ekram Hossain,et al.  Dynamic Spectrum Access and Management in Cognitive Radio Networks: Introduction , 2009 .

[22]  Ekram Hossain,et al.  Dynamic Spectrum Access and Management in Cognitive Radio Networks , 2009 .

[23]  Nicolò Cesa-Bianchi,et al.  Gambling in a rigged casino: The adversarial multi-armed bandit problem , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

[24]  Jeffrey H. Reed,et al.  Defense against Primary User Emulation Attacks in Cognitive Radio Networks , 2008, IEEE Journal on Selected Areas in Communications.

[25]  Zhu Han,et al.  Dogfight in Spectrum: Jamming and Anti-Jamming in Multichannel Cognitive Radio Systems , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[26]  S. Anand,et al.  An Analytical Model for Primary User Emulation Attacks in Cognitive Radio Networks , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[27]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[28]  William A. Arbaugh,et al.  Dynamic spectrum access in cognitive radio networks , 2006 .

[29]  Y. Freund,et al.  The non-stochastic multi-armed bandit problem , 2001 .

[30]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[31]  M. K. Ghosh,et al.  Zero-Sum Stochastic Games with Partial Information , 2004 .