Subspace-based spectrum guarding

Cognitive radio is regarded as the most promising technology for driving the future development of next-generation wireless networks, increasing spectrum efficiency by dynamic spectrum access. Allowing white spaces to be used in a deregulated manner exposes the radio spectrum to anomalous usage, which in turn may intentionally or unintentionally cause interference to primary users (PUs) as well as secondary users (SUs). In this paper we propose a subspace-based spectrum guarding detector (SSGD) to address the security of unauthorized spectrum usage. In particular, we consider the scenario where ongoing authorized transmissions by PUs have to be preserved from concurrent unauthorized emissions in the same frequency bands. The detector operates in two phases: a training phase where SSGD collects samples of the authorized transmissions, and a detection (on-line) phase where the spectrum is guarded against anomalous emissions. The performance of SSGD is analyzed in terms of detection and false alarm probabilities highlighting the effects of the propagation scenario, the PU characteristics, the signal-to-noise ratio (SNR) and the duration of both training and on-line phases. Numerical results show that in practical scenarios SSGD provides a remarkable SNR improvement of several dB over the conventional energy detector (ED).

[1]  Mani B. Srivastava,et al.  Localization in Cognitive Radio Systems In The Presence of Spatially Obfuscated Data , 2012 .

[2]  Zhu Han,et al.  Securing Collaborative Spectrum Sensing against Untrustworthy Secondary Users in Cognitive Radio Networks , 2010, EURASIP J. Adv. Signal Process..

[3]  T. Charles Clancy,et al.  Security in Cognitive Radio Networks: Threats and Mitigation , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

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

[5]  Louis L. Scharf,et al.  Matched subspace detectors , 1994, IEEE Trans. Signal Process..

[6]  Ming-Tuo Zhou,et al.  Design considerations of IEEE 802.15.4m low-rate WPAN in TV white space , 2013, IEEE Communications Magazine.

[7]  Marco Chiani,et al.  Recent Advances on Wideband Spectrum Sensing for Cognitive Radio , 2014 .

[8]  Robert C. Qiu,et al.  Kernel Feature Template Matching for Spectrum Sensing , 2014, IEEE Transactions on Vehicular Technology.

[9]  Alexandros G. Fragkiadakis,et al.  A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks , 2013, IEEE Communications Surveys & Tutorials.

[10]  Thomas Kailath,et al.  Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..

[11]  Mohsen Guizani,et al.  Securing cognitive radio networks against primary user emulation attacks , 2015, IEEE Network.

[12]  Francisco J. Perales López,et al.  Adding Image Constraints to Inverse Kinematics for Human Motion Capture , 2010, EURASIP J. Adv. Signal Process..

[13]  Santosh Pandey,et al.  IEEE 802.11af: a standard for TV white space spectrum sharing , 2013, IEEE Communications Magazine.

[14]  M. Wicks,et al.  Target detection with linear and kernel subspaces matching in the presence of strong clutter , 2012, 2012 IEEE Radar Conference.

[15]  Zhu Han,et al.  CatchIt: Detect Malicious Nodes in Collaborative Spectrum Sensing , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[16]  Moe Z. Win,et al.  Estimating the number of signals observed by multiple sensors , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[17]  Yuan Shi,et al.  Detecting Primary User Emulation Attack in Cognitive Radio Networks , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[18]  Andrea Giorgetti,et al.  Spectrum holes detection by information theoretic criteria , 2011, CogART '11.

[19]  Sai Shankar Nandagopalan,et al.  IEEE 802.22: An Introduction to the First Wireless Standard based on Cognitive Radios , 2006, J. Commun..

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

[21]  Liu Dian,et al.  Localization in Cognitive Radio Systems in the Presence of Spatially Obfuscated Data , 2013 .

[22]  Andrea Giorgetti,et al.  Model Order Selection Based on Information Theoretic Criteria: Design of the Penalty , 2015, IEEE Transactions on Signal Processing.

[23]  Nei Kato,et al.  Intrusion detection system (IDS) for combating attacks against cognitive radio networks , 2013, IEEE Network.

[24]  Tongtong Li,et al.  Mitigating primary user emulation attacks in cognitive radio networks using advanced encryption standard , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[25]  Andrea Giorgetti,et al.  Wideband spectrum sensing for cognitive radio: A model order selection approach , 2014, 2014 IEEE International Conference on Communications (ICC).

[26]  Li Xiao,et al.  Defense against Spectrum Sensing Data Falsification Attacks in Cognitive Radio Networks , 2011, SecureComm.

[27]  Larry J. Greenstein,et al.  ALDO: An Anomaly Detection Framework for Dynamic Spectrum Access Networks , 2009, IEEE INFOCOM 2009.