IEICE TRANSACTIONS on Communications
Attacker Detection Based on Dissimilarity of Local Reports in Collaborative Spectrum Sensing
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E95-B No.9 pp.3024-3027
- Publication Date
- 2012/09/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E95.B.3024
- Type of Manuscript
- LETTER
- Category
- Wireless Communication Technologies
Authors
Keyword
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Junnan YAO, Qihui WU, Jinlong WANG, "Attacker Detection Based on Dissimilarity of Local Reports in Collaborative Spectrum Sensing" in IEICE TRANSACTIONS on Communications,
vol. E95-B, no. 9, pp. 3024-3027, September 2012, doi: 10.1587/transcom.E95.B.3024.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E95.B.3024/_p
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@ARTICLE{e95-b_9_3024,
author={Junnan YAO, Qihui WU, Jinlong WANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Attacker Detection Based on Dissimilarity of Local Reports in Collaborative Spectrum Sensing},
year={2012},
volume={E95-B},
number={9},
pages={3024-3027},
abstract={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.},
keywords={},
doi={10.1587/transcom.E95.B.3024},
ISSN={1745-1345},
month={September},}
Copy
TY - JOUR
TI - Attacker Detection Based on Dissimilarity of Local Reports in Collaborative Spectrum Sensing
T2 - IEICE TRANSACTIONS on Communications
SP - 3024
EP - 3027
AU - Junnan YAO
AU - Qihui WU
AU - Jinlong WANG
PY - 2012
DO - 10.1587/transcom.E95.B.3024
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E95-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2012
AB - 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.
ER -
English
