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Pre‐processing of incomplete spectrum sensing data in spectrum sensing data falsification attacks detection: a missing data imputation approach
Author(s) -
Yao Junnan,
Cao Jianjun,
Zheng Qibin,
Ma Jingang
Publication year - 2016
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2015.1111
Subject(s) - imputation (statistics) , missing data , computer science , cognitive radio , imperfect , data mining , sensor fusion , machine learning , telecommunications , wireless , linguistics , philosophy
Attacks detection is an important issue in collaborative spectrum sensing (CSS) system of cognitive radio networks. Many approaches are proposed to cope with the malicious behaviour of attackers. In existing works, spectrum sensing data (SSD) received by the fusion centre is generally assumed to be integrated. However, in practical scenarios, the received SSD may be incomplete due to the imperfect reporting channel or specific CSS schemes. The performance of existing attacks detection approaches may degrade especially when the probability of missing data is large. To alleviate this challenge, the authors focus on pre‐processing of incomplete SSD and propose a practical imputation algorithm, which is derived from the maximum a posteriori probability criterion, to fill in the missing values. Simulation results indicate that the proposed algorithm restores the characteristics of the SSD, and mitigate the impacts of missing value on existing attacks detection algorithm effectively.

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