Securing the future of wireless communications : Utilizing a blend of deep learning techniques for identifying risks in advanced communication networks
Megha Jainmeghajain37@gmail.comSchool of Computing Science and EngineeringVIT Bhopal UniversityBhopal, Madhya Pradesh, 466114, IndiaView full profile → , Ravi Vermaravi.verma@vitbhopal.ac.inSchool of Computing Science and EngineeringVIT Bhopal UniversityBhopal, Madhya Pradesh, 466114, IndiaView full profile → , Sunil Kumarsunil.kumard@jaipur.manipal.eduDepartment of IoT & Intelligent SystemsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , *Atul SrivastavaCorresponding authoratul.nd2@gmail.comDepartment of Computer Science and EngineeringAmity School of Engineering and TechnologyLucknow, Uttar Pradesh, 201313, IndiaView full profile → , Anuradha Pillaianuradha.pillai@sitpune.edu.inDepartment of Computer Science and EngineeringSymbiosis Institute of TechnologyPune, Maharashtra, 412115, IndiaView full profile → , Vijay Shankar Sharmavijayshankar.sharma@jaipur.manipal.eduDepartment of Computer and Communication EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 11 Oct 2024
- Published Online:
- 31 May 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1819
- Pages:
- 939–948
Abstract
Keywords
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References
[1] G. Zachos, I. E. Livieris, K. G. Margaritis, P. Pintelas, and S. Petridou, “An anomaly-based intrusion detection system for Internet of Medical Things networks,” Electronics, vol. 10, no. 21, p. 2562 (2021).
[2] H. H. Pajouh, R. Javidan, R. Khayami, D. Ali, and K.-K. R. Choo, “Two-tier network anomaly detection model: A machine learning approach,” Journal of Intelligent Information Systems, vol. 48, pp. 61–74 (2017).
[3] S. M. Kasongo and Y. Sun, “Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset,” Journal of Big Data, vol. 7, no. 1, p. 105 (2020).
[4] M. Mittal, V. Sharma, R. Garg, M. Singh, and S. Verma, “Machine learning techniques for energy efficiency and anomaly detection in hybrid wireless sensor networks,” Energies, vol. 14, no. 11, p. 3125 (2021).
[5] E. Jaw and X. Wang, “Feature selection and ensemble-based intrusion detection system: An efficient and comprehensive approach,” Symmetry, vol. 13, no. 10, p. 1764 (2021).
[6] H. W. Oleiwi, M. A. Al-Rawi, A. H. Khudayer, A. A. S. Al-Waisy, and T. Abdellatif, “MLTS-ADCNS: Machine learning techniques for anomaly detection in communication networks,” IEEE Access, vol. 10, pp. 91006–91017 (2022).
[7] D. N. Mhawi, H. A. Jalab, R. A. Al-Khateeb, and A. A. M. Dinar, “Advanced feature-selection-based hybrid ensemble learning algorithms for network intrusion detection systems,” Symmetry, vol. 14, no. 7, p. 1461 (2022).
[8] S. Q. Mohammed and M. A. E. S. Hussein, “An innovative multi-dataset performance analysis of machine learning classifiers based on features reduction for intrusion detection,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 8s, pp. 553–569 (2024).
[9] B. Xu, J. Liu, Q. Liu, H. Guo, and Y. Zhang, “Strengthening network security: Deep learning models for intrusion detection with optimized feature subset and effective imbalance handling,” Computers, Materials & Continua, vol. 78, no. 2 (2024).
[10] HoaNP, NSL-KDD Dataset, GitHub repository, 2024. [Online]. Available: https://github.com/HoaNP/NSL-KDD-DataSet.git [Accessed: Jun. 14, 2024].
[11] D. Gaikwad and R. Thool, “DAREnsemble: Decision tree and rule learner based ensemble for network intrusion detection system,” in Proc. First Int. Conf. on Information and Communication Technology for Intelligent Systems: Volume 1, S. C. Satapathy and S. Das, Eds. Cham: Springer International Publishing, pp. 185–193 (2016).
[12] N. Kanakarajan and K. Muniasamy, “Improving the accuracy of intrusion detection using GAR-Forest with feature selection,” in Proc. First Int. Conf. on Information and Communication Technology for Intelligent Systems, pp. 539–547 (2016).




