Opportunistic transmission using hybrid sensing for Cognitive Radio Sensor Network in the presence of smart Primary User Emulation Attack

Vishnu, J; Bhagyaveni, M. A.

doi:10.1080/00207217.2020.1837254

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Increased throughput and reduced energy consumption are attained with the hybrid spectrum sensing scheme. Opportunistic data transmission utilizing the hybrid spectrum sensing scheme for cognitive radio sensor networks in the presence of primary user emulation attack (PUEA) is proposed in Bala Vishnu and Bhagyaveni 28 . Hybrid spectrum sensing scheme which effectively combines both the proactive and reactive sensing schemes reduces the error probability in detecting the PU signal.…”

Section: Introductionmentioning

confidence: 99%

Adaptive coalition with Kullback‐Leibler divergence for cooperative spectrum sensing (KLDCSS) in cognitive radio networks

Munisamy,

A Bhagyaveni

2024

Int J Communication

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SummaryEffective, reliable, and rapid spectrum sensing in concordance with maximum throughput efficiency is necessary at the cognitive radio network to maximize the network's utilization. An adaptive coalition using Kullback–Leibler divergence (KLD)‐based cooperative spectrum sensing scheme (KLDCSS) with a nominal sensing time to optimize the spectral efficiency is presented in this paper. In a coalition‐based spectrum sensing scheme, cooperating secondary users (SUs) are allocated to different coalitions to sense different Primary User (PU) channels. The same cognitive users allocated to sense the primary user channels are allotted to detect in different sensing phases. In the proposed sensing scheme, the cooperating SUs are adaptively allocated to different coalitions to maximize the throughput efficiency. Collaborating cognitive users with a standard signal‐to‐noise ratio and distinct energy levels that are characterized by the localized probabilities of detection and false alarm is considered. An adaptive coalition and user allocation algorithm are proposed to optimize the average opportunistic throughput and diminish the sensing overhead. The expression for throughput efficiency in terms of average throughput and sensing accuracy is derived. In this scheme, the SU computes the log‐likelihood ratio (LLR) from the acquired sample and forwards the same to the fusion center. The fusion center then accumulates the LLR values computed by the SU to estimate the likelihood of spectrum availability. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput efficiency over various numbers of SUs, coalitions, and signal‐to‐noise ratios, are presented.

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Section: Introductionmentioning

confidence: 99%

Adaptive coalition with Kullback‐Leibler divergence for cooperative spectrum sensing (KLDCSS) in cognitive radio networks

Munisamy,

A Bhagyaveni

2024

Int J Communication

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“…In ML-based methods, the hyperparameter setting is the major issue that leads to inaccurate attack detection. For PUE detection, localization methods play a pivotal role [19]. The localization methods determine the accurate location of the signal source and then compare the location with the original location in order to detect the PUE attack accurately.…”

Section: Introductionmentioning

confidence: 99%

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

Pari

Jaisankar

2022

Symmetry

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The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy.

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Spectrally Efficient Cognitive Relaying for IoT Networks

Vishnu

Lavanya

Bhagyaveni

2021

Wireless Pers Commun

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Opportunistic transmission using hybrid sensing for Cognitive Radio Sensor Network in the presence of smart Primary User Emulation Attack

Cited by 7 publications

References 28 publications

Adaptive coalition with Kullback‐Leibler divergence for cooperative spectrum sensing (KLDCSS) in cognitive radio networks

Adaptive coalition with Kullback‐Leibler divergence for cooperative spectrum sensing (KLDCSS) in cognitive radio networks

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

Spectrally Efficient Cognitive Relaying for IoT Networks

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