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

被引:95
作者
Li, Husheng [1 ]
Han, Zhu [2 ]
机构
[1] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA
[2] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
基金
美国国家科学基金会;
关键词
Cognitive radio; abnormality detection; spectrum sensing;
D O I
10.1109/TWC.2010.091510.100315
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:3554 / 3565
页数:12
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