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All Journal Journal of ICT Research and Applications
Narendra Kumar
Department of CSE, Amity University Jharkhand, Ranchi, 835303, Jharkhand,
Computer Science & IT

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Enhancing IoT Cybersecurity with Multi-Layer Deep Transfer Learning Approach for Intrusion Detection Anuj Rapaka; Govindan Manoharan Karthik; Balla Sudhir; Gurram Venkata Naga Bhagya Sree; Narendra Kumar; Jyothi Nelahonne Mohan
Journal of ICT Research and Applications Vol. 19 No. 3 (2026)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2026.19.3.4

Abstract

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.
Co-Authors Anuj Rapaka Balla Sudhir Govindan Manoharan Karthik Gurram Venkata Naga Bhagya Sree Jyothi Nelahonne Mohan