A novel enhanced neural network for anomaly detection in the IoT environment - Inuwa, Muhammad Muhammad; Das, Resul (Pergamon-Elsevier Science Ltd, 2026)
The Internet of Things (IoT) is changing many fields, including healthcare, transportation, and smart cities. However, the rise of serious cybersecurity issues. This paper introduces a new architecture of Fully connected neural networks (FCNNs) designed for detecting anomalies in IoT settings. The proposed model uses residual connections, multi-head attention mechanisms, SHAP-based feature selection, and RAdam optimisation to improve detection accuracy, robustness, and interpretability. Extensive tests on two benchmark datasets, ToN-IoT and UNSW-NB15, show that the model works well. Specifically, it achieved an accuracy of 99. 98% and an F1 score of 99. 95% in the ToN-IoT dataset, as well as an accuracy and F1 score of 99. 74% in the UNSW-NB15 dataset. These findings suggest that the improved FCNNs architecture can effectively identify anomalies while being lightweight enough for use in real-time IoT systems. The addition of explainability techniques further enhances its usefulness in critical cybersecurity situations by providing precise and interpretable predictions.
Keywords: Anomaly detection; IoT; Cybersecurity; FCNNs; Deep learning