Current - Issue

1. M. A. Talukder, M. M. Islam, M. A. Uddin, K. F. Hasan, S. Sharmin, S. A. Alyami, and M. A. Moni. Machine learning-based network
intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction. Journal of Big
Data. 2024;11:33.
2. R. A. Disha and S. Waheed. Performance analysis of machine learning models for intrusion detection system using Gini impurity-based
weighted random forest (GIWRF) feature selection technique. Cybersecurity. 2022;5(1).
3. Q. Sun. Spoofing attack detection using machine learning in heterogeneous wireless networks. Wireless Communications and Mobile
Computing. 2021.
4. N. Karmous et al. Deep learning approaches for protecting IoT devices in software-defined networking (SDN) environments against man-
in-the-middle (MitM) attacks. 2024.
5. R. A. Sowah, K. B. Ofori-Amanfo, G. A. Mills, and K. M. Koumadi. Detection and prevention of man-in-the-middle spoofing attacks in
MANETs using predictive techniques in artificial neural networks (ANN). Journal of Computer Networks and Communications. 2019.
6. M. H. Behiry and M. Aly. Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and
machine learning methods. Journal of Big Data. 2024;11:16.
7. A. F. Mukeri and D. P. Gaikwad. Adversarial machine learning attacks and defenses in network intrusion detection systems. International
Journal of Wireless and Microwave Technologies. 2022;12(1):12–21.
8. H. Bazzi, A. Nassar, M. El Bizri, and A. M. Haidar. A practical intrusion detection approach for ARP spoofing and MITM in local area
networks. BAU Journal – Science and Technology. 2024;6(1).
9. V. Singh and R. Makani. Machine learning approaches for network attack detection: A comprehensive review of techniques, chall enges,
and future directions. IJRASET Journal for Research in Applied Science and Engineering Technology. 2025.
10. U. O. Obonna, F. K. Opara, C. C. Mbaocha, J.-K. Obichere, I. O. Akwukwaegbu, M. M. Amaefule, and C. I. Nwakanma. Detection of
man-in-the-middle (MitM) cyber-attacks in oil and gas process control networks using machine learning algorithms. Future Internet.
2023;15(8):280.
11. N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki. Network intrusion detection for IoT security based on learning
techniques. IEEE Communications Surveys & Tutorials. 2019;21(3):2671–2701.
12. M. Bhavsar, K. Roy, J. Kelly, and O. Olusola. Anomaly-based intrusion detection system for IoT application. Discover Internet of Things.
2023;3(1):5.
13. S. Tsimenidis, T. Lagkas, and K. Rantos. Deep learning in IoT intrusion detection. Journal of Network and Systems Management.
2022;30(1):8.
14. N. Islam, F. Farhin, I. Sultana, M. S. Kaiser, M. S. Rahman, M. Mahmud, A. S. M. S. Hosen, and G. H. Cho. Towards machine learning
based intrusion detection in IoT networks. Computers, Materials & Continua. 2021;69(2):1801–1821.
15. B. Susilo and R. F. Sari. Intrusion detection in IoT networks using deep learning algorithm. Information. 2020;11(5):279.
16. M. M. Rahman, S. Al Shakil, and M. R. Mustakim. A survey on intrusion detection system in IoT networks. Cyber Security and
Applications. 2025;3:100082.
17. Y. Zhang, P. Li, and X. Wang. Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access.
2019;7:31711–31722.
18. A. Zohourian, S. Dadkhah, H. Molyneaux, E. C. P. Neto, and A. A. Ghorbani. IoT-PRIDS: Leveraging packet representations for intrusion
detection in IoT networks. Computers & Security. 2024;146:104034.
19. V. Kumar, A. K. Das, and D. Sinha. UIDS: A unified intrusion detection system for IoT environment. Evolutionary Intelligence.
2021;14(1):47–59.
20. T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj. Anomaly-based intrusion detection system for IoT networks through deep
learning model. Computers and Electrical Engineering. 2022;99:107810.
21. S. Abbas, A. Al Hejaili, G. A. Sampedro, M. Abisado, A. S. Almadhor, T. Shahzad, and K. Ouahada. A novel federated edge learning
approach for detecting cyberattacks in IoT infrastructures. IEEE Access. 2023;11:112189–112198.
22. M. Sajid, K. R. Malik, A. Almogren, T. S. Malik, A. H. Khan, J. Tanveer, and A. U. Rehman. Enhancing intrusion detection: A h ybrid
machine and deep learning approach. Journal of Cloud Computing. 2024;13(1):123.
23. D. A. Sivasakthi, A. Sathiyaraj, and R. Devendiran. HybridRobustNet: Enhancing detection of hybrid attacks in IoT networks through
advanced learning approach. Cluster Computing. 2024;27(4):5005–5019.
24. M. Ali, M. Shahroz, M. F. Mushtaq, S. Alfarhood, M. Safran, and I. Ashraf. Hybrid machine learning model for efficient botnet attack
detection in IoT environment. IEEE Access. 2024;12:40682–40699.
25. A. Alfatemi, M. Rahouti, D. F. Hsu, C. Schweikert, N. Ghani, A. Solyman, and M. I. S. Assaqty. Identifying distributed denial of service
attacks through multi-model deep learning fusion and combinatorial analysis. Journal of Network and Systems Management. 2025;33(1):8.
26. A. Abbas, M. Salahuddin, M. Z. Khan, A. A. Khan, F. U. Zaman, S. A. Inam, G. Aldehim, T. Mazhar, and M. A. Khan. Machine learning-
based hybrid technique to enhance cyber-attack perspective. Journal of Cloud Computing. 2025;14(1):1–23.
27. V. Kandasamy and A. A. Roseline. Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-
middle cyber attacks. Scientific Reports. 2025;15(1):1697.
28. M. Saritha and S. Malgireddy. Integrating hybrid deep learning architecture with enhanced feature selection techniques to mitigate multiple
attacks. Cluster Computing. 2026;29(1):68.
29. A. Anuradha, A. S. Chouhan, and S. Srinivas Rao. Improving malware detection performance using hybrid deep representation learning
with heuristic search algorithms. Scientific Reports. 2026.
30. N. Singh and R. Agarwal. Hybrid Net: Enhanced DTL-based intrusion detection system for electric vehicular network using hybrid
architecture. Peer-to-Peer Networking and Applications. 2026;19(1):1.