Single decision reporting for cooperative spectrum sensing under erroneous feedback channels with Byzantine attack

Chouhan, Ankit; Captain, Kamal M.; Parmar, Ashok; Kumar, Rahul

doi:10.1016/j.phycom.2022.101891

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…A series of secure and efficient CSS strategies in references 27–29 to combat Byzantine attack in different network scenarios and conditions. Under Byzantine attack, a single decision reporting‐ CSS was developed in reference 30. In reference 31, a closed‐form detector for blind spectrum sensing utilizing one‐bit observations was devised.…”

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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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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show abstract

“…Wi‐Fi and other wireless technologies contains usage limit, which is a limited‐use scenario owing to licensing policies 3–5 . To overcome these problems in cognitive radio networks, intermediate users share the licensed user or primary user frequency band on non‐interference basis 6 . Adaptive use of the licensed band when not in use by the primary user enables cognitive radio networks to offer devices with long‐range higher bandwidth network access 7,8 .…”

Section: Introductionmentioning

confidence: 99%

“…[3][4][5] To overcome these problems in cognitive radio networks, intermediate users share the licensed user or primary user frequency band on non-interference basis. 6 Adaptive use of the licensed band when not in use by the primary user enables cognitive radio networks to offer devices with long-range higher bandwidth network access. 7,8 Cognitive Radio Networks help for the upcoming 5G.…”

Section: Introductionmentioning

confidence: 99%

Byzantine attack detection using optimized dual‐channel capsule generative adversarial network for securing cognitive radio network

A.,

S.,

T. M.

et al. 2023

Trans Emerging Tel Tech

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Cognitive Radio Networks (CRN) is emerged as a solution for the problem of spectrum scarcity as it achieves transmission opportunities in under‐utilized spectrum bands for primary users (PUs). Therefore, in this manuscript, an efficient Byzantine attack detection using optimized Dual‐Channel Capsule Generative Adversarial Network and Auto‐Metric Graph Neural Network (AMGNN) to secure Cognitive Radio Network. Here, two types of layers are utilized that is processing layer and decision layer. In processing layer, the dataset generation based on emission matrix from Hidden Markov Model (HMM) contains labeled data for the legitimate secondary users including malicious Byzantine attackers. In decision layer, the generated dataset is given to Dual‐Channel Capsule Generative Adversarial Network (DCCGAN) to classify secondary users (SUs) as Normal or Attacker. Normally, DCCGAN not exposes any optimization techniques adoption to compute optimum parameters for assuring exact prediction. Thus, capuchin search algorithm (CSA) is employed for optimizing DCCGAN weight parameters. Once the attack is detected, then alert information is fed to AMGNN opportunistic spectrum access. Proposed model executed at MATLAB and performance analyzed with performance metrics like system utility rate Vs available spectrum range, Attack detection rate, Accuracy, Packet Delivery Ratio (PDR), and throughput. The proposed method attains higher accuracy of 96.31%, 97.06%, 93.52%, higher system utility rate of 97.22%, 91.39%, 96.41%, and higher attack detection rate of 96.25%, 94.12%, 97.15% analyzed to the existing models, such as BAD‐AESA‐SCRN, BAD‐BMHHO‐ENN‐SCRN, and BAD‐HCM‐EELM‐SCRN respectively.

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Reinforcement learning for performance improvement in cooperative spectrum sensing

Kumar

Parmar

Captain

et al. 2023

Physical Communication

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Single decision reporting for cooperative spectrum sensing under erroneous feedback channels with Byzantine attack

Cited by 6 publications

References 26 publications

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

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

Byzantine attack detection using optimized dual‐channel capsule generative adversarial network for securing cognitive radio network

Reinforcement learning for performance improvement in cooperative spectrum sensing

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