Catch Me if You Can: An Abnormality Detection Approach for Collaborative Spectrum Sensing in Cognitive Radio Networks

• Published in: IEEE transactions on wireless communications Vol. 9; no. 11; pp. 3554 - 3565
• Main Authors: Li, Husheng, Han, Zhu
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; abnormality detection; Applied sciences; Cognitive radio; Collaboration; Data integration; Data mining; Detection; Detection algorithms; Exact sciences and technology; History; Infinity; Information theory; Information, signal and communications theory; Measurement; Networks; Radio; Radio networks; Radiocommunication specific techniques; Radiocommunications; Sensors; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; spectrum sensing; Strategy; Telecommunications; Telecommunications and information theory; Wireless communication; Performance evaluation; Data processing; Data mining; Cognitive radio; Cognitive system; False alarm rate; abnormality detection; Spectrum analysis; Information processing; spectrum sensing; Data fusion; Numerical simulation; Bayes detection; Signal analysis; Radio communication; Software radio
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2010.091510.100315

Collaborative spectrum sensing is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and then exclude the attacker's report for spectrum sensing. Many existing attacker-detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian attacker detection. However, in practical cognitive radio systems the data fusion center typically does not know the attacker's strategy. To alleviate the problem of the unknown strategy of attacker(s), an abnormality-detection approach, based on the abnormality detection in data mining, is proposed. The performance of the attacker detection in the single-attacker scenario is analyzed explicitly. For the case in which the attacker does not know the reports of honest secondary users (called independent attack), it is shown that the attacker can always be detected as the number of spectrum sensing rounds tends to infinity. For the case in which the attacker knows all the reports of other secondary users, based on which the attacker sends its report (called dependent attack), an approach for the attacker to perfectly avoid being detected is found, provided that the attacker has perfect information about the miss-detection and false-alarm probabilities. This motivates cognitive radio networks to protect the reports of secondary users. The performance of attacker detection in the general case of multiple attackers is demonstrated using numerical simulations.