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IJHCS
Published by Research Parks Publishing
ISSN : 26151898     EISSN : 26158159     DOI : https://doi.org/10.31149/ijhcs.v7i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Neuroscience
The International Journal of Human Computing Studies (IJHCS) publishes original research over the whole spectrum of work relevant to the theory and practice of modern interactive systems of the contemporary world. IJHCS accepts papers in forms of original research articles, review articles, book reviews, case reports, and discussions to answer important and interesting questions of the innovative research points.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
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Search results for , issue "Vol. 8 No. 1 (2026): International Journal of Human Computing Studies (IJHCS)" : 1 Documents clear
A Hybrid Anomaly Detection Framework Integrating Self-Supervised Learning and Ensemble Intelligence for Securing IoT Networks Shakir, Hussein Ali; Ahmed, Ahmed Younus
International Journal on Human-Computing Studies Vol. 8 No. 1 (2026): International Journal of Human Computing Studies (IJHCS)
Publisher : Research Parks Publishers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijhcs.v8i1.5643

Abstract

The fast-tracking development of the Internet of Things (IoT) has led to the networks being subjected to very complex and innovative cyberattacks. As a result, the need for a reliable anomaly detection system has become a severe challenge. In this paper, a hybrid anomaly detection framework called HADES-IoT is presented that is equipped with self-supervised representation learning and ensemble intelligence for the safeguarding of IoT networks. The architecture comprises a deep autoencoder for self-supervised feature embedding, a LightGBM classifier for supervised decision learning, and an Isolation Forest for unsupervised anomaly detection. When applying the TON_IoT dataset from public resources, HADES-IoT achieved nearly perfect results, attaining a ROC-AUC of 0.9998, PR-AUC of 0.9999, and an overall accuracy of 99.85%. The framework not only shows the capacity of strong generalization over the unseen traffic patterns but also through the use of SHAP-based explainability it is demonstrated that the features of packet-level, flow-derived and a few of the latent autoencoder components are the most influential ones in the anomaly detection. The zero-day simulations, on the other hand, underscore the detection of the previously unseen attacks ability of HADES-IoT by the utilization of the unsupervised embeddings. The proposed system is capable of providing a hybrid defense strategy that is scalable, interpretable, and suitable for future IoT infrastructures.

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