Secured Soft Combination Schemes Against Malicious-Users in Cooperative Spectrum Sensing

Gul, Noor; Qureshi, Ijaz Mansoor; Naveed, A.; Elahi, Atif; Rasool, Imtiaz

doi:10.1007/s11277-019-06408-9

Cited by 14 publications

(12 citation statements)

References 40 publications

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“…Trust-based mechanism for malicious user detected in CSS was proposed in [17]. Similar recent works can be found in [18,19] and references therein. However, the previous studies have considered the attacks restricted only to specific type and not in general.…”

Section: Background and Motivationmentioning

confidence: 66%

Improved likelihood ratio statistic‐based cooperative spectrum sensing for cognitive radio

2020

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Section: Background and Motivationmentioning

confidence: 66%

Improved likelihood ratio statistic‐based cooperative spectrum sensing for cognitive radio

2020

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“…Yuan et al in [27] proposed a maximum mean discrepancy (MMD)-based secure fusion strategy to defend against intelligent SSDF attack for collaborative spectrum sensing in CRNs. Gul et al in [28] outlined different techniques to mitigate the damaging effects of the false sensing in soft decision fusion (SDF) schemes using one-to-many sensing-distances and Z-score.…”

Section: Related Workmentioning

confidence: 99%

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

Song

et al. 2020

IET Communications

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“…As demand to the frequency spectrum resources is increasing with the increased number of wireless devices, therefore, static spectrum allocation (SSA) policy is considered to have limitations to meet these requirements [5]. The 300 GHz bandwidth that once seems to be sufficient is now becoming congested [6][7][8][9]. CRN is an intelligent wireless communication technology that has the ability to sense the radio environment and act accordingly.…”

Section: Introductionmentioning

confidence: 99%

“…The PU spectrum availability is inspected by applying various spectrum sensing techniques [6]. The SU performs local sensing by adopting sensing techniques such as energy detector (ED), matched filter detector (MFD), and cyclostationary [7]. When statistics of the PU are not available, then the ED technique is more suitable that requires only power of the PU channel.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Analysis of Different Outlier Detection Techniques in Cognitive Radio Networks with Malicious Users

Ahmed

Khan

Gul

et al. 2020

Wireless Communications and Mobile Computing

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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.

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Secured Soft Combination Schemes Against Malicious-Users in Cooperative Spectrum Sensing

Cited by 14 publications

References 40 publications

Improved likelihood ratio statistic‐based cooperative spectrum sensing for cognitive radio

Improved likelihood ratio statistic‐based cooperative spectrum sensing for cognitive radio

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

A Comparative Analysis of Different Outlier Detection Techniques in Cognitive Radio Networks with Malicious Users

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