Citation

Li, Justin Ting Lau and Pang, Ying Han and Zarakovitis, Charilaos C. and Lim, Heng Siong and Skordoulis, Dionysis and Ooi, Shih Yin and Chan, Kah Yoong and Pang, Wai Leong (2025) BiTAD: An Interpretable Temporal Anomaly Detector for 5G Networks with TwinLens Explainability. Future Internet, 17 (11). p. 482. ISSN 1999-5903

Text futureinternet-17-00482.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

The transition to 5G networks brings unprecedented speed, ultra-low latency, and massive connectivity. Nevertheless, it introduces complex traffic patterns and broader attack surfaces that render traditional intrusion detection systems (IDSs) ineffective. Existing rule-based methods and classical machine learning approaches struggle to capture the temporal and dynamic characteristics of 5G traffic, while many deep learning models lack interpretability, making them unsuitable for high-stakes security environments. To address these challenges, we propose Bidirectional Temporal Anomaly Detector (BiTAD), a deep temporal learning architecture for anomaly detection in 5G networks. BiTAD leverages dual-direction temporal sequence modelling with attention to encode both past and future dependencies while focusing on critical segments within network sequences. Like many deep models, BiTAD’s faces interpretability challenges. To resolve its “black-box” nature, a dual-perspective explainability module, coined TwinLens, is proposed. This module integrates SHAP and TimeSHAP to provide global feature attribution and temporal relevance, delivering dual-perspective interpretability. Evaluated on the public 5G-NIDD dataset, BiTAD demonstrates superior detection performance compared to existing models. TwinLens enables transparent insights by identifying which features and when they were most influential to anomaly predictions. By jointly addressing the limitations in temporal modelling and interpretability, our work contributes a practical IDS framework tailored to the demands of next-generation mobile networks.

Item Type:	Article
Uncontrolled Keywords:	5G networks, anomaly detection, temporal modelling, attention, explainable AI
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	12 Dec 2025 01:15
Last Modified:	13 Dec 2025 14:39
URII:	http://shdl.mmu.edu.my/id/eprint/15069

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