Research on spectrum sensing data falsification attack detection algorithm in cognitive Internet of Things

Miao, Liu; Xu, Di; Huo, Zhuo-Miao; Sun, Zhenxing

doi:10.1007/s11235-022-00896-0

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…In Reference 11, Fu and He introduced a Bayesian‐inference‐based sliding‐window trust model to efficiently detect and remove independent and collaborative probabilistic SSDF attackers, even without any knowledge of the attack patterns. In addition, Miao et al 12 proposed a spectrum sensing strategy to employ weighted combining of the hidden Markov model. In References 13–15, the authors tried to defend large‐scale attacks with the help of trusted SU.…”

Section: Motivation and Contributionmentioning

confidence: 99%

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

Zhu,

Wu,

et al. 2024

Trans Emerging Tel Tech

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To improve spectrum utilization, cognitive radio (CR) enables unauthorized internet of thing (IoT) devices to opportunistically access the channel underutilized by the primary user (PU) in a cognitive IoT (CIoT). To this end, cooperative spectrum sensing (CSS) plays a crucial role in CR technology, which leverages the spatial diversity of corroborative IoT devices to accurately detect the PU signal. However, this open cooperative paradigm may suffer from spectrum sensing data falsification (SSDF) attack in which malicious IoT devices intentionally mislead the fusion center (FC) by providing false sensing results to make an incorrect global decision regarding the PU status. To effectively characterize the attack behavior of malicious IoT devices, we propose a massive SSDF attack model described by the attack cycle and malicious ratio within a sensing period to characterize the malicious behaviors. Additionally, we introduce a delivery evaluation mechanism and propose a dynamic sliding window‐CSS (DSW‐CSS) to mitigate the impact of massive SSDF attack. Moreover, we introduce a sequential reporting mechanism to further reduce the number of samples required by the global decision‐making of the FC. Finally, simulation results show the flexibility and aggressiveness of the proposed attack model and demonstrate the correctness and effectiveness of DSW‐CSS.

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Section: Motivation and Contributionmentioning

confidence: 99%

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

Zhu,

Wu,

et al. 2024

Trans Emerging Tel Tech

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“…On basis of the weighted combination of the hidden Markov model, a CSS strategy against SSDF attack was proposed in the internet of thing. 12 In reference 13, a secure estimation secure diffusion least-mean-square algorithm against both Byzantine and manipulation attack was proposed. A modified outlier removal spectrum sensing scheme was proposed to conditionally remove outlier in reference 14.…”

Section: Related Work and Our Contributionsmentioning

confidence: 99%

“…In reference 11, based on a soft combining, a butterfly optimization algorithm was proposed to find an optimal weight coefficient vector against Byzantine attack. On basis of the weighted combination of the hidden Markov model, a CSS strategy against SSDF attack was proposed in the internet of thing 12 . In reference 13, a secure estimation secure diffusion least‐mean‐square algorithm against both Byzantine and manipulation attack was proposed.…”

Section: Introductionmentioning

confidence: 99%

Less sample‐cooperative spectrum sensing against large‐scale Byzantine attack in cognitive wireless sensor networks

Wu,

Dai,

Teng

et al. 2024

Trans Emerging Tel Tech

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Cooperative spectrum sensing (CSS) has emerged as a promising strategy for identifying available spectrum resources by leveraging spatially distributed sensors in cognitive wireless sensor networks (CWSNs). Nevertheless, this open collaborative approach is susceptible to security threats posed by malicious sensors, specifically Byzantine attack, which can significantly undermine CSS accuracy. Moreover, in extensive CWSNs, the CSS process imposes substantial communication overhead on the reporting channel, thereby considerably diminishing cooperative efficiency. To tackle these challenges, this article introduces a refined CSS approach, termed weighted sequential detection (WSD). This method incorporates channel state information to validate the global decision made by the fusion center and assess the trust value of sensors. The trust value based weight is assigned to sensing samples, which are then integrated into a sequential detection framework within a defined time window. This sequential approach prioritizes samples based on descending trust values. Numerical simulation results reveal that the proposed WSD outperforms conventional fusion rules in terms of the error probability, sample size, achievable throughput, and latency, even under varying degrees of Byzantine attack. This innovation signifies a substantial advancement in enhancing the reliability and efficiency of CSS.

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A Lightweight Secure Throughput Optimization Scheme in Cognitive Radio Networks

Tofiq,

Fathi,

Ahmed

2023

Wireless Pers Commun

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Research on spectrum sensing data falsification attack detection algorithm in cognitive Internet of Things

Cited by 6 publications

References 38 publications

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

Less sample‐cooperative spectrum sensing against large‐scale Byzantine attack in cognitive wireless sensor networks

A Lightweight Secure Throughput Optimization Scheme in Cognitive Radio Networks

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