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...