Enhancing IoT Cybersecurity with Multi-Layer Deep Transfer Learning Approach for Intrusion Detection

Anuj Rapaka (Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (Autonomous), Bhimavaram, 534202)
Govindan Manoharan Karthik (Department of Information Security,School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Katpadi - Thiruvalam Road, Vellore - 632 014, Tamil Nadu,)
Balla Sudhir (Department of Electronics and Communication, International School of Technology and Sciences for Women, NH-16, Eastgonagudem, Rajanagaram, Rajamahendravaram, AP-533294,)
Gurram Venkata Naga Bhagya Sree (Department of Computer Science, Anil Neerukonda Institute of Technology & Sciences (ANITS),Sangivalasa, Bheemunipatnam, Visakhapatnam, Andhra Pradesh-531162,)
Narendra Kumar (Department of CSE, Amity University Jharkhand, Ranchi, 835303, Jharkhand,)
Jyothi Nelahonne Mohan (Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation,Vaddeswaram, Andhra Pradesh, 522502)

Journal of ICT Research and Applications
Vol. 19 No. 3 (2026)

Intrusion detection in IoT-enabled cloud environments is challenged by high-dimensional traffic, class imbalance, and limited labeled data. This paper proposes a hybrid framework combining Golden Jackal–Grey Wolf Optimization (GJO-GWO) for feature selection with a Kernel Mean Alignment Autoencoder (KMA-AE) for deep transfer learning. GJO-GWO selects a compact, discriminative feature subset, while KMA-AE aligns source and target latent representations to mitigate distribution mismatch. Experiments on the CIDDS-001 dataset achieve 90.21% accuracy and 0.90 macro-F1, with improved precision–recall for minority attacks and a 60% feature reduction. Although training is more expensive, the method attains the lowest inference time, enabling real-time deployment. Overall, the framework provides an effective and generalizable intrusion detection solution for dynamic IoT environments.