Abstract
In a cognitive radio (CR), opportunistic secondary users (SUs) periodically sense the primary user’s (PU’s) existence in the network. Spectrum sensing of a single SU is not precise due to wireless channels and hidden terminal issues. One promising solution is cooperative spectrum sensing (CSS) that allows multiple SUs’ cooperation to sense the PU’s activity. In CSS, the misdetection of the PU signal by the SU causes system inefficiency that increases the interference to the system. This paper introduces a new category of a malicious user (MU), i.e., a lazy malicious user (LMU) with two operating modes such as an awakened mode and sleeping mode. In the awakened mode, the LMU reports accurately the PU activity like other normal cooperative users, while in the sleeping mode, it randomly reports abnormal sensing data similar to an always yes malicious user (AYMU) or always no malicious user (ANMU). In this paper, statistical analysis is carried out to detect the behavior of different abnormal users and mitigate their harmful effects. Results are collected for the different hard combination schemes in the presence of the LMU and opposite categories of malicious users (OMUs). Simulation results collected for the error probability, detection probability, and false alarm at different levels of the signal-to-noise ratios (SNRs) and various contributions of the LMUs and OMUs confirmed that out of the many outlier detection tests, the median test performs better in MU detection by producing minimum error probability results in the CSS. The results are further compared by keeping minimum SNR values with the mean test, quartile test, Grubbs test, and generalized extreme studentized deviate (GESD) test. Similarly, performance gain of the median test is examined further separately in the AND, OR, and voting schemes that show minimum error probability results of the proposed test as compared with all other outlier detection tests in discarding abnormal sensing reports.
Highlights
Radio spectrum is considered the backbone for wireless communication
The proposed hard decision fusion (HDF) schemes show better sensing results with minimum error probabilities, while the simple HDF schemes result in maximum error probability
As the signal-to-noise ratios (SNRs) are increased to -10 dB, the percent decrease in error probability of the proposed HDF schemes is further improved for the proposed voting (9.8%), proposed OR (1.7%), and proposed AND (47.1%) schemes compared with the simple voting, simple OR, and simple AND decision schemes
Summary
Radio spectrum is considered the backbone for wireless communication. The unique characteristic of the wireless sensor networks (WSNs) makes it distinguishable from the traditional networks [1]. These attackers usually form a collusive group that can boost the spectrum sensing data falsification (SSDF) attack power, resulting in falsification of the spectrum sensing data These attackers are prevented by applying a trust mechanism technique, in which the reports of the SUs are examined by their historical sensing behaviors [22]. As the awakened phase sensing reports of the LMUs are accurate, the outlier detection tests declare their sensing reports as normal and suggest for consideration in the global decision (iii) Simulation verifies that, out of the many outlier detection tests, the median test shows better detection results of MUs in CSS and produces minimum error probability.
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