Jurnal SINTA 2
Enhancing IoT Cybersecurity with Multi-Layer Deep Transfer Learning Approach for Intrusion Detection
Journal of ICT Research and Applications (JICTRA) · Vol. 19 No. 3 (2026) · pp. 275–292
- ISSN
- 2338-5499, 2337-5787
- Published
- 2026-05-26
- Publisher
- DRI - ITB
- Language
- eng
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.
Keywords
Collaboration network of the authors
6 authors · 15 co-authorship links · 3 across different organisations
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- Vellore Institute of Technology University
- Anil Neerukonda Institute of Technology & Sciences
- Koneru Lakshmaiah Education Foundation
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
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