Attacker Detection Based on Dissimilarity of Local Reports in Collaborative Spectrum Sensing

Junnan YAO  Qihui WU  Jinlong WANG  

Publication
IEICE TRANSACTIONS on Communications   Vol.E95-B   No.9   pp.3024-3027
Publication Date: 2012/09/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E95.B.3024
Print ISSN: 0916-8516
Type of Manuscript: LETTER
Category: Wireless Communication Technologies
Keyword: 
collaborative spectrum sensing,  dissimilarity metric,  dissimilarity-based attacker detection,  

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Summary: 
In this letter, we propose a dissimilarity metric (DM) to measure the deviation of a cognitive radio from the network in terms of local sensing reports. Utilizing the probability mass function of the DM, we present a dissimilarity-based attacker detection algorithm to distinguish Byzantine attackers from honest users. The proposed algorithm is able to identify the attackers without a priori information of the attacking styles and is robust against both independent and dependent attacks.