For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Attacker Detection Based on Dissimilarity of Local Reports in Collaborative Spectrum Sensing
Junnan YAO Qihui WU Jinlong WANG
IEICE TRANSACTIONS on Communications
Publication Date: 2012/09/01
Online ISSN: 1745-1345
Print ISSN: 0916-8516
Type of Manuscript: LETTER
Category: Wireless Communication Technologies
collaborative spectrum sensing, dissimilarity metric, dissimilarity-based attacker detection,
Full Text: PDF(337.1KB)>>
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.