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Lightweight Flow-based Anomaly Detection for IoT Using HC-MTDNN: A Hierarchically Cascaded Multitask Deep Neural Network, Journal of Telecommunications and Information Technology, 2025
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Tytuł: Lightweight Flow-based Anomaly Detection for IoT Using HC-MTDNN: A Hierarchically Cascaded Multitask Deep Neural Network, Journal of Telecommunications and Information Technology, 2025

Autor:

Beghoura, Mohamed Amine ; Belouche, Younes

Typ zasobu:

artykuł

Tytuł publikacji grupowej:

2025, nr 4, JTIT-artykuły

Opis:

kwartalnik

Abstrakt:

In this article, we propose a lightweight, hierarchical multi-task learning framework designed for detecting both high-level and fine-grained threats in IoT traffic. The developed model focuses on anomalies detectable through flow-level metadata. The deliberate choice to prioritize computational efficiency by excluding content analysis scopes the approach to payload-independent threats, while still enabling robust detection of key attack classes. To further enhance efficiency within this metadata-driven paradigm, we introduce HC-MTDNN, a hierarchical multitask model that integrates a gated feature mechanism and feature reuse to significantly reduce redundancy and computational overhead, improving upon previous hierarchical architectures and achieving high performance while dealing with volumetric and protocol-based attacks. The model is evaluated on four benchmark datasets: CICIoT2023, N-BaIoT, Bot-IoT, and Edge-IIoTset. It demonstrates strong performance in both binary and multiclass classification tasks, with an average inference time of 122 us per sample and a compact model size of 2.4 MB. The proposed framework effectively balances accuracy and computational efficiency, offering a practical and scalable solution for securing resource-constrained IoT environments.

Numer:

4

Wydawca:

National Institute of Telecommunications

Identyfikator zasobu:

oai:bc.itl.waw.pl:2420

DOI:

10.26636/jtit.2025.4.2311

eISSN:

on-line: ISSN 1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Licencja:

CC BY 4.0

Temat i słowa kluczowe:

anomaly detection deep neural network IoT security lightweight model multitask learning network traffic analysis

Kolekcje, do których przypisany jest obiekt:

Data ostatniej modyfikacji:

7 sty 2026

Data dodania obiektu:

7 sty 2026

Liczba wyświetleń treści obiektu:

2

Wszystkie dostępne wersje tego obiektu:

https://bc.itl.waw.pl/publication/2739

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Wyświetl opis w formacie OAI-PMH:

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Nazwa wydania Data
Beghoura, Mohamed Amine, Lightweight Flow-based Anomaly Detection for IoT Using HC-MTDNN: A Hierarchically Cascaded Multitask Deep Neural Network, Journal of Telecommunications and Information Technology, 2025 7 sty 2026

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