Sequential 0/1 for Cooperative Spectrum Sensing in the Presence of Strategic Byzantine Attack

Wu, Jun; Yu, Yue; Song, Tiecheng; Hu, Jing

doi:10.1109/lwc.2018.2877665

Cited by 34 publications

(21 citation statements)

References 10 publications

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“…Considering that the unavailable prior knowledge, the maximized likelihood estimation is made based on the extended sensing to optimize the optimal performance. In [17], J. Wu et al proposed a low-complexity sequential 0/1 defense strategy against strategic Byzantine attack for CSS. Both [18] and [19] made use of estimation algorithm to estimate attack parameters, aiming to adopt appropriate defense strategies.…”

Section: Related Workmentioning

confidence: 99%

Throughput and Interference for Cooperative Spectrum Sensing: A Malicious Perspective

2021

KSII TIIS

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Cognitive radio (CR) is a feasible intelligent technology and can be used as an effective solution to spectrum scarcity and underutilization. As the key function of CR, cooperative spectrum sensing (CSS) is able to effectively prevent the harmful interference with primary users (PUs) and identify the available spectrum resources by exploiting the spatial diversity of multiple secondary users (SUs). However, the open nature of the cognitive radio networks (CRNs) framework makes CSS face many security threats, such as, the malicious user (MU) launches Byzantine attack to undermine CRNs. For this aim, we make an in-depth analysis of the motive and purpose from the MU's perspective in the interweave CR system, aiming to provide the future guideline for defense strategies. First, we formulate a dynamic Byzantine attack model by analyzing Byzantine behaviors in the process of CSS. On the basis of this, we further make an investigation on the condition of making the fusion center (FC) blind when the fusion rule is unknown for the MU. Moreover, the throughput and interference to the primary network are taken into consideration to evaluate the impact of Byzantine attack on the interweave CR system, and then analyze the optimal strategy of Byzantine attack when the fusion rule is known. Finally, theoretical proofs and simulation results verify the correctness and effectiveness of analyses about the impact of Byzantine attack strategy on the throughput and interference.

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Section: Related Workmentioning

confidence: 99%

Throughput and Interference for Cooperative Spectrum Sensing: A Malicious Perspective

2021

KSII TIIS

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“…ognitive Radio Networks (CRNs) provides a promising solution to reliable and time-efficient spectrum utilization by mitigating the spectrum scarcity issues created by the requirement for wireless bandwidth growing significantly due to the explosive growth of wireless devices [1]. It enables primary user (PU) networks to share their spectrum with the secondary users (SUs), on condition that that the SU's transmission does not affect the PU's performance adversely.…”

Section: Introductionmentioning

confidence: 99%

“…It enables primary user (PU) networks to share their spectrum with the secondary users (SUs), on condition that that the SU's transmission does not affect the PU's performance adversely. Notwithstanding, CRNs have been found to be susceptible to external malicious threats, due to the broadcast nature of the PU-SU cooperation transmission techniques which may allow for a malicious eavesdropper to acquire the transmission information of Pus [1]. The ability to sense accurately the presence of PU is of the absolute importance of CRNs.…”

Section: Introductionmentioning

confidence: 99%

“…In situations where individual SU sensing is shadowed by severe multipath fading and shadowing effects, the SU may not reliably detect the PU signal and access the channel when there is a PU signal present leading to interference of the licensed PU [1,2]. To overcome this hidden terminal problem and increase the spectrum sensing credibility, cooperative spectrum sensing (CSS) has been studied in [1,3]. In CSS, each sensing result from multiple SU are sent and fused at a data fusion centre (FC).…”

Section: Introductionmentioning

confidence: 99%

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Robust Statistics Evidence Based Secure Cooperative Spectrum Sensing for Cognitive Radio Networks

Simpson

Sun

2020

2020 International Wireless Communications and Mobile Computing (IWCMC)

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Cognitive radio networks (CRNs), an assemble of smart schemes intended for permitting secondary users (SUs) to opportunistically access spectral bands vacant by primary user (PU), has been deliberated as a solution to improve spectrum utilization. Cooperative spectrum sensing (CSS) is a vital technology of CRN systems used to enhance the PU detection performance by exploiting SUs' spatial diversity, however CSS leads to spectrum sensing data falsification (SSDF), a new security threat in CR system. The SSDF by malicious users can lead to a decrease in CSS performance. In this work, we propose a CSS scheme in which the presence and absence hypotheses distribution of PU signal is estimated based on past sensing received energy data incorporating robust statistics, and the data fusion are performed according to an evidence based approach. Simulation results show that the proposed scheme can achieve a significant malicious user reduction due to the abnormality of the distribution of malicious users compared with that of other legitimate users. Furthermore, the performance of our data fusion scheme is improved by supplemented nodes' credibility weight.

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“…Cooperative spectrum sensing (CSS) can significantly improve detection performance by combining sensing results of SUs in different geographical locations [4, 5]. The probability of these SUs experiencing multiple paths, shadowing, or hidden user problems simultaneously is very low.…”

Section: Introductionmentioning

confidence: 99%

Performance improvement for machine learning‐based cooperative spectrum sensing by feature vector selection

et al. 2020

IET Communications

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Sequential 0/1 for Cooperative Spectrum Sensing in the Presence of Strategic Byzantine Attack

Cited by 34 publications

References 10 publications

Throughput and Interference for Cooperative Spectrum Sensing: A Malicious Perspective

Throughput and Interference for Cooperative Spectrum Sensing: A Malicious Perspective

Robust Statistics Evidence Based Secure Cooperative Spectrum Sensing for Cognitive Radio Networks

Performance improvement for machine learning‐based cooperative spectrum sensing by feature vector selection

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