Volume 15 , Issue 2 , PP: 131-146, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Rania Aboalela 1 *
Doi: https://doi.org/10.54216/JCIM.150211
As a dynamic paradigm, Cognitive radio networks (CRNs) in wireless transmission enable devices to intelligently adapt their communication parameter based on real-world spectrum availability. Spectrum sensing lies at the core of CRNs, where nodes continue to monitor the spectrum for underutilized or unused band detection. However, the presence of malicious users (MUs) has a significant impact reliability and performance of the network. MUs detection is indispensable to prevent interference or unauthorized access and ensure network integrity. Advanced techniques combining game theory, machine learning, and signal processing are used for effectively identifying and mitigating malicious activities. CRNs can ensure efficient spectrum utilization and enhance security in heterogeneous and dynamic environments by incorporating robust MU detection systems into spectrum sensing protocols. This article presents a Malicious User Recognition using the Coot Optimization Algorithm with Bayesian Belief Network (MUR-COABBN) technique for CRN. The MUR-COABBN technique exploits metaheuristics with a Bayesian machine-learning method for the classification of the MUs in the CRN. In the MUR-COABBN technique, the COA is initially used to choose better feature subsets. Moreover, the detection of MUs can be performed by the use of BBN. Finally, the parameter tuning of the BBN model is carried out using an improved seeker optimization algorithm (ISOA). The experimental evaluation of the MUR-COABBN technique takes place with respect to distinct aspects. The experimentation outcomes implied the improved performance of the MUR-COABBN methodology with other methods under distinct measures. Therefore, the MUR-COABBN model can effectually and accurately improve security in the CRN.
Cognitive Radio Network , Metaheuristics , Malicious User Recognition , Coot Optimization Algorithm , Machine Learning , Parameter Tuning
[1] Jain, N. Gupta, and M. Sreenu, ‘‘Blockchain based smart contract for cooperative spectrum sensing in cognitive radio networks for sustainable beyond 5G wireless communication,’’ Green Technol. Sustainability, vol. 1, no. 2, May 2023, Art. no. 100019.
[2] M. K. Giri and S. Majumder, ‘‘Extreme learning machine based identification of malicious users for secure cooperative spectrum sensing in cognitive radio networks,’’ Wireless Pers. Commun., vol. 130, no. 3, pp. 1993–2012, Jun. 2023.
[3] S. K. Agrawal, A. Samant, and S. K. Yadav, ‘‘Spectrum sensing in cognitive radio networks and metacognition for dynamic spectrum sharing between radar and communication system: A review,’’ Phys. Commun., vol. 52, Jun. 2022, Art. no. 101673.
[4] A. Khanna, P. Rani, T. H. Sheikh, D. Gupta, V. Kansal, and J. J. P. C. Rodrigues, ‘‘Blockchain-based security enhancement and spectrum sensing in cognitive radio network,’’ Wireless Pers. Commun., vol. 127, no. 3, pp. 1899–1921, Dec. 2022.
[5] A. Upadhye, P. Saravanan, S. S. Chandra, and S. Gurugopinath, ‘‘A survey on machine learning algorithms for applications in cognitive radio networks,’’ in Proc. IEEE Int. Conf. Electron., Comput. Commun. Technol. (CONECCT), Jul. 2021, pp. 01–06.
[6] S. K. Ram, ‘‘Energy-efficient adaptive sensing for cognitive radio sensor network in the presence of primary user emulation attack,’’ Comput. Electr. Eng., vol. 106, Mar. 2023, Art. no. 108619.
[7] H. Jiang, Z. Yu, and J. Yang, ‘‘Research on key technology of full duplex cognitive radio network,’’ in Proc. J. Phys., Conf., May 2021, vol. 1920, no. 1, Art. no. 012035.
[8] M. Arkwazee, M. Ilyas, and A. Dawood Jasim, ‘‘Automatic spectrum sensing techniques using support vector machine in cognitive radio network,’’ in Proc. 2nd Int. Conf. Adv. Electr., Comput., Commun. Sustain. Technol. (ICAECT), Apr. 2022, pp. 1–6.
[9] A. Shirolkar and S. V. Sankpal, ‘‘Deep learning based performance of cooperative sensing in cognitive radio network,’’ in Proc. 2nd Global Conf. for Advancement Technol. (GCAT), Oct. 2021, pp. 1–4.
[10] K. Arshid, Z. Jianbiao, I. Hussain, G. G. Lema, M. Yaqub, and R. Munir, ‘‘Support vector machine approach of malicious user identification in cognitive radio networks,’’ Wireless Netw., 2022.
[11] Almuqren, L., Maray, M., Alotaibi, F.A., Alzahrani, A., Mahmud, A. and Rizwanullah, M., 2024. Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks. IEEE Access.
[12] Benazzouza, S., Ridouani, M., Salahdine, F. and Hayar, A., 2022. A novel prediction model for malicious users detection and spectrum sensing based on stacking and deep learning. Sensors, 22(17), p.6477.
[13] Gong, Q., Liu, Y., Zhang, J., Chen, Y., Li, Q., Xiao, Y., Wang, X. and Hui, P., 2023. Detecting malicious accounts in online developer communities using deep learning. IEEE Transactions on Knowledge and Data Engineering.
[14] Maray, M., Maashi, M., Alshahrani, H.M., Aljameel, S.S., Abdelbagi, S. and Salama, A.S., 2024. Intelligent Pattern Recognition using Equilibrium Optimizer with Deep Learning Model for Android Malware Detection. IEEE Access.
[15] Maniriho, P., Mahmood, A.N. and Chowdhury, M.J.M., 2023. API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques. Journal of Network and Computer Applications, 218, p.103704.
[16] Chaganti, R., Ravi, V. and Pham, T.D., 2022. Deep learning based cross architecture internet of things malware detection and classification. Computers & Security, 120, p.102779.
[17] Liu, H., Han, F. and Zhang, Y., 2024. Malicious traffic detection for cloud-edge-end networks: A deep learning approach. Computer Communications, 215, pp.150-156.
[18] Aurangzeb, S. and Aleem, M., 2023. Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism. Scientific Reports, 13(1), p.3093.
[19] Kaligineedi, P., Khabbazian, M. and Bhargava, V.K., 2010. Malicious user detection in a cognitive radio cooperative sensing system. IEEE Transactions on Wireless Communications, 9(8), pp.2488-2497.
[20] Naser, A.T., Mohammed, K.K., Ab Aziz, N.F., binti Kamil, K. and Mekhilef, S., 2024. Improved coot optimizer algorithm-based MPPT for PV systems under complex partial shading conditions and load variation. Energy Conversion and Management: X, p.100565.
[21] Yu, Z., Dong, H., Guo, T. and Zhao, B., 2024, February. A Multi-Surrogate Assisted Salp Swarm Feature Selection Algorithm with Multi-Population Adaptive Generation Strategy for Classification. In Asian Conference on Machine Learning (pp. 1590-1605). PMLR.
[22] Rabbi, M., Ali, S.M., Kabir, G., Mahtab, Z. and Paul, S.K., 2020. Green supply chain performance prediction using a Bayesian belief network. Sustainability, 12(3), p.1101.
[23] Yue, C., Zhao, X., Tao, L., Zheng, C., Ding, Y. and Guo, Y., 2024. An Improved Seeker Optimization Algorithm for Phase Sensitivity Enhancement of a Franckeite-and WS2-Based SPR Biosensor for Waterborne Bacteria Detection. Micromachines, 15(3), p.362.
