2026· International Conference on Security and Cryptography· pp. 358-365· 0 citations· 17 references
Computer Science
Abstract
: The hierarchical and heterogeneous nature of Internet of Things (IoT) architectures, spanning edge, fog, and cloud layers, makes intrusion detection particularly challenging, as each layer provides only a partial view of the system. Traditional intrusion detection systems (IDS) often fail to identify coordinated and distributed attacks from fragmented observations. This paper proposes HTEF-IDS, a hierarchical intrusion detection framework that combines LSTM-based temporal modeling with Dempster–Shafer evidence fusion to infer a global security state from distributed observations. Local detections are progressively aggregated across layers, while a feedback mechanism dynamically adjusts detection sensitivity according to the inferred threat level. Experiments conducted on the CIC IoT-DIAD 2024 dataset show that the proposed framework improves detection performance, reduces false positives, and effectively identifies distributed attack patterns that are not observable at a single layer.
The results indicate that EHiD-FC supports interpretable and imbalance-aware intrusion diagnosis in Fog–Cloud IoT environments while highlighting the need for further validation under large-scale real-world deployment conditions.
Ashutosh Shankhdhar, A. Sivakumaran, Nithya Rekha Sivakumar et al.· Scientific Reports· 0 citations
Cloud-centric internet of things (IoT) environments continuously generates heterogeneous network traffic, making intrusion detection increasingly challenging as attack behaviors evolve beyond previously observed patterns. Conventional intrusion detection systems (IDS) rely primarily on supervised classification, limiti...
Jawahar Muthusamy, Palani Latha, R. Ponnumani et al.· Bulletin of Electrical Engin...· 0 citations
A family-aware hierarchical intrusion detection framework for attack-family prediction that first separates normal and attack traffic, then routes attack samples into empirically defined majority and minority attack-family branches, and finally performs branch-specific family classification.
Motab F. Alenezi, F. Alotaibi, B. Alturki et al.· Computer Modeling in Enginee...· 0 citations
A reliability-aware edge–cloud framework that treats early detection as a sequential routing problem, and identifies the minimum-evidence gate and cloud-refinement stage as the main reliability controls.
Siraj Azam, Farheen Naaz, Mikail Mohammed Salim· Electronics· 0 citations
In a comparative review of forty peer-reviewed studies, it is demonstrated that hybrid DL models provide excellent detection performance (99-100% classification accuracy on benchmark datasets) as well as practical viability for deployment with privacy-preserving Federated Learning for large-scale data.
Mohammed Gharkan, Mustafa I. Hussien Al-Janabi, Obaid Salim· Al-Noor Journal of Engineeri...· 0 citations
A role-based multi-agent cybersecurity framework for 6G-enabled IoT networks that enables proactive intrusion prediction, real-time detection, and knowledge-driven threat mitigation and a promising step toward scalable and intelligent cybersecurity management in 6G-enabled IoT networks is proposed.
Alaeddine Diaf, A. A. Korba, W. Jaafar et al.· IEEE Open Journal of the Com...· 0 citations
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