A Trust Framework to Detect Malicious Nodes in Cognitive Radio Networks

Rathee, Geetanjali; Ahmad, Farhan; Kerrache, Chaker Abdelaziz; Azad, Muhammad Ajmal

doi:10.3390/electronics8111299

Cited by 16 publications

(9 citation statements)

References 42 publications

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“…Briefly put, traditional optimization techniques are unsuitable for CR-IoT applications due to the lack of a complete environment model [19], the fact that they are not scalable to support massive IoT connectivity [20,21], and the fact that they are unable to adapt to the changing and dynamic environments [21]. Many different intelligent MU detections have been developed by researchers for empowering user security such as FoF [22], TTA [23], and so on that are discovered with high accuracy.…”

Section: Optimization and Heuristic Approachesmentioning

confidence: 99%

Blockchain‐oriented location privacy preserving for cooperative spectrum sensing in 6G wireless networks

Roopa

Pradhan

2023

IET Blockchain

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In this article, the authors proposed a novel blockchain-oriented location privacypreserving (BoLPP) for the Cooperative Spectrum Sensing (CSS) in 6G networks. In order to attain the sustainability of privacy and security for 6G wireless networks, it is a great challenge in this sensing as it faces various malicious attacks while the secondary user (SU) is active. To tackle these issues, the authors proposed a novel framework for blockchainoriented Cognitive Radio Networks (CRNs) for CSS using an energy detection technique. Moreover, the authors implemented another novel paradigm BoLPP, to attain the privacy of SUs location for CSS in 6G networks. This approach preserves the SUs' location and makes the BoLPP framework immune to all malicious attackers. The simulation results have been undergone based on the performance metrics such as response time, consistency, probability of false alarm, frame loss (%), average network throughput, energy efficiency, and security. The outcomes reveal that the proposed scheme achieves high security, privacy, energy efficiency, average network throughput, and low probability of false alarm and frame loss (%) when compared with the existing frameworks such as Friend or Foe (FoF) and Tidal Trust Algorithm (TTA) mechanisms. It is observed that the proposed BoLPP mechanism provides better security and privacy in 6G wireless networks.

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Section: Optimization and Heuristic Approachesmentioning

confidence: 99%

Blockchain‐oriented location privacy preserving for cooperative spectrum sensing in 6G wireless networks

Roopa

Pradhan

2023

IET Blockchain

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“…A heavier shadowing severity of the satellite interference link results is used to minimise the outage probability. A secure and trusted routing and handoff mechanism were introduced in [36] to improve the network delay and overhead. However, the inter‐domain handoff communication was not considered.…”

Section: Related Workmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically. Unlicensed users severely affect the spectrum sensing outcomes in CRN. Primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) have become a paramount concern in CRNs. It is especially challenging when both masquerading (in the physical layer) and falsification (in the data link layer) occur by providing false spectrum reports. Existing methods to detect such attacks cannot be utilised in scenarios with multi-hop CRN. In this study, to mitigate attack against PUEA and SSDF, a method called optimised sensing and Levenshtein nearest centroid classification (OS-LNCC) for multi-hop CRN is presented. First, a network model for multi-hop CRN is designed. Next, a probable density optimal logical sensing model is designed to alleviate the problems related to falsification of spectrum reports. Here, the falsification of spectrum reports is overcome by exploiting dual factors, that is, probability for false alarm and probability for detection according to the departure rate of primary user (PU). With these dual factors, optimal logical sensing is made, therefore improving the throughput with minimum delay. Finally, each cognitive radio (CR) user evaluates its current sensing information to existing sensing classes through the Levenshtein distance function. Based on quantitative variables, the prediction function of each sensing class is measured using nearest centroid (NC) classifier and the sensing report is classified into either presence or absence of PU. These predictive classes are then integrated at the fusion centre so that robust mitigation against PUEA and SSDF is made. Computer simulation outputs show that OS-LNCC method performs better than the conventional methods using metrics such as sensing delay by 47%, percentage of error in prediction by 46% and throughput by 45%.

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“…A trust analyser (TA) is proposed between the CR nodes. The TA handles the table of entire transmitting nodes while computing the trust value through the social impact theory optimizer [9].…”

Section: Introductionmentioning

confidence: 99%

Hermitan matrices based malicious cognitive radio detection and bayesian method for detecting primary user emulation attack

Jeyakumar

Shanmathi

Smitha

et al. 2023

IJEECS

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Cognitive radio (CR) is a facilitating technology to efficiently deal with the spectrum scarceness, and it will significantly enhance the spectrum deployment of upcoming wireless transmission method. Security is a significant concern, although not well tackle in cognitive radio networks (CRN). In CR networks, this approach regard as a security issue happen from primary user emulation attack (PUEA). A PUEA attacker forwards an emulated primary signal and defraud the CR users to avoid them from accessing spectrum holes. Here, we introduce a Hermitan matrices based malicious cognitive radio (CMCR) detection and Bayesian method for detecting PUEA attack in the CRN. In this approach, the Bayesian method is used for detecting the PUEA attack. The trust analyzer evaluates the CR trust. Here, the node trust value is computed by node activeness and inactiveness, degree of data transmission, and hermitan matrics verification. In addition, the Hermitan Matrices method is used to detect the malicious CR user in the CRN. The simulation outcomes propose that the CMCR leads to improve the performance in terms of better detection ratio, minimized the possibility of miss detection ratio. Furthermore, it minimized the possibility of false alarm in the CRN.

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A Trust Framework to Detect Malicious Nodes in Cognitive Radio Networks

Cited by 16 publications

References 42 publications

Blockchain‐oriented location privacy preserving for cooperative spectrum sensing in 6G wireless networks

Blockchain‐oriented location privacy preserving for cooperative spectrum sensing in 6G wireless networks

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Hermitan matrices based malicious cognitive radio detection and bayesian method for detecting primary user emulation attack

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