Authors
Roland N Mfondoum, Antoni Ivanov, Nikol Gotseva, Atanas Vlahov, Pavlina Koleva, Vladimir Poulkov
Published in
Sensors (Basel, Switzerland). Volume 26. Issue 20. Oct 12, 2026. Epub Oct 12, 2026.
Abstract
The number of Internet of Things (IoT) devices has been growing rapidly, driven by the demand for low-cost and low-power devices across a wide range of industrial and consumer applications. This expansion has broadened the attack surface exposed to cyber threats, making the detection of malicious activity in the IoT infrastructure a pressing research challenge. Most existing approaches are limited to binary anomaly detection, rely on a single data modality, or incur a computational overhead incompatible with realistic deployment on the edge. This paper introduces the Multi-modal Universal Informer Convolutional Neural Network (MUI-CNN) Classifier, a distributed architecture for multi-class attack detection across heterogeneous IoT sensor and network environments. It combines four components - a multi-modal universal data embedding layer that unifies numerical, categorical, and temporal features into a shared spatio-temporal representation, a CNN local pattern recognition layer, an encoder-only classifier for long-term temporal dependencies, and an optional statistical masking layer for greater sensitivity to low-amplitude signatures. The model is evaluated on the Telecommunication Operating Systems (OS) and Network Internet of Things (TON-IoT) dataset covering eight sensor types and ten attack classes. Results demonstrate F1 above 88% on all IoT sensors and up to 94.85% on the network sensor, confirming that each architectural component contributes a measurable and distinct improvement over partial baselines.
PMID:
42944305
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.
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