International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 521KB)
Adaptive Trust-Based Malicious user Detection in Spectrum Sensing for Cognitive Radio Networks using AI and Blockchain
PDF (521KB), PP.31-43
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Author(s)
Amith K S
1,*
Sridhara T.
1
Usha G. R.
2
Index Terms
Cognitive radio networks, malicious user detection, spectrum sensing, machine learning, blockchain, anomaly detection, adaptive trust management
Abstract
Malicious user detection in spectrum sensing is a critical challenge in Cognitive Radio Networks (CRNs). Traditional rule-based mechanisms lack adaptability to dynamic behaviors, while existing AI techniques often overlook scalability and real-time constraints. This paper proposes a novel hybrid framework that integrates adaptive trust-based mechanisms with AI-powered anomaly detection and blockchain technology to achieve superior detection accuracy (>90%), energy efficiency (30% reduction), and scalability (supporting 500+ nodes with blockchain throughput >750 transactions/second). The framework dynamically updates trust scores using machine learning models and leverages blockchain for secure and transparent spectrum management. Comparative simulations demonstrate superior performance compared to existing methods. The proposed methodology addresses the limitations of static trust mechanisms and offers a robust solution for real-time malicious user detection in CRNs.
Cite This Paper
Amith K. S., Sridhara T., Usha G. R., "Adaptive Trust-Based Malicious User Detection in Spectrum Sensing for Cognitive Radio Networks Using AI and Blockchain", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.4, pp. 31-43, 2026. DOI:10.5815/ijwmt.2026.04.03
Reference
[1]I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, "NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey," Computer Networks, vol. 50, no. 13, pp. 2127-2159, 2006. DOI: 10.1016/j.comnet.2006.05.001
[2]Y. Arjoune and N. Kaabouch, "A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions," Sensors, vol. 19, no. 1, p. 126, 2019. DOI: 10.3390/s19010126
[3]T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications," IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, 2009. DOI: 10.1109/SURV.2009.090109
[4]L. Zhang, G. Ding, Q. Wu, Y. Zou, Z. Han, and J. Wang, "Byzantine Attack and Defense in Cognitive Radio Networks: A Survey," IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1342-1363, 2015. DOI: 10.1109/COMST.2015.2422735
[5]W. Wang, A. Kwasinski, D. Niyato, and Z. Han, "Security in cognitive radio networks: Threats and mitigation," IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 381-396, 2013. DOI: 10.1109/SURV.2012.030512.00030
[6]A. Thilina, K. W. Choi, N. Saquib, and E. Hossain, "Machine learning techniques for cooperative spectrum sensing in cognitive radio networks," IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2209-2221, 2013. DOI: 10.1109/JSAC.2013.131120
[7]S. Wang, H. Wang, Z. Peng, J. Yang, S. Cui, and C. Li, "Machine Learning for Cognitive Radio Networks," IEEE Communications Magazine, vol. 56, no. 4, pp. 158-164, 2018. DOI: 10.1109/MCOM.2018.1700683
[8]M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, "Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study," Journal of Information Security and Applications, vol. 50, 102419, 2020. DOI: 10.1016/j.jisa.2019.102419
[9]Y. Dai, D. Xu, S. Maharjan, Z. Chen, Q. He, and Y. Zhang, "Blockchain and deep reinforcement learning empowered intelligent 5G beyond," IEEE Network, vol. 33, no. 3, pp. 10-17, 2019. DOI: 10.1109/MNET.2019.1800376
[10]H. Kim, J. Park, M. Bennis, and S.-L. Kim, "Blockchained on-device federated learning," IEEE Communications Letters, vol. 24, no. 6, pp. 1279-1283, 2020. DOI: 10.1109/LCOMM.2020.2979208
[11]Z. Yang, M. Chen, K. K. Wong, H. V. Poor, and S. Cui, "Federated learning for 6G," IEEE Communications Magazine, vol. 58, no. 6, pp. 30-36, 2020. DOI: 10.1109/MCOM.001.1900609
[12]S. Benazzouza, M. Ridouani, F. Salahdine, and A. Hayar, "A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning," Sensors, vol. 22, no. 17, p. 6477, 2022. DOI: 10.3390/s22176477
[13]E. E. Ezhilarasi and J. C. Clement, "GRU-SVM Based Threat Detection in Cognitive Radio Network," Sensors, vol. 23, no. 3, p. 1326, 2023. DOI: 10.3390/s23031326
[14]D. Cuellar, M. Sallal, and C. Williams, "BSM-6G: Blockchain-Based Dynamic Spectrum Management for 6G Networks: Addressing Interoperability and Scalability," IEEE Access, vol. 12, pp. 59643-59664, 2024. DOI: 10.1109/ACCESS.2024.3393288
[15]Q. Li, Q. Wang, H. Zhao, T. Chang, Y. Yang, and S. Xia, "Dynamic Credible Spectrum Sharing Based on Smart Contract in Vehicular Networks," Mathematics, vol. 12, no. 13, p. 1929, 2024. DOI: 10.3390/math12131929