Wavelet-Based Intelligent Framework for Network Traffic Anomaly Detection in IoT Embedded Systems
DOI:
https://doi.org/10.59461/ijdiic.v5i1.242Keywords:
IoT Security , Network Traffic , Wavelet Transform , Anomaly Detection , Deep LearningAbstract
The rapid growth of the Internet of Things (IoT) and embedded systems has increased the vulnerability of network infrastructures to cyber-attacks, necessitating efficient real-time anomaly detection methods. In this study, we introduce a primary IoT network traffic dataset generated through controlled simulation of normal and malicious behaviors. Both time-domain features, including packet size, inter-arrival time, protocol type, and TCP flags, and frequency-domain features, such as spectral entropy and band energy derived via wavelet transform, were extracted to capture comprehensive traffic characteristics. These features were used to train a hybrid deep learning model, the Adaptive Differential Evolution Weighted Deep Belief Network (ADE-WDBN), which combines deep hierarchical feature learning with evolutionary weight optimization to enhance detection accuracy and computational efficiency. Experimental results demonstrate that ADE-WDBN outperforms traditional machine learning models and conventional deep learning approaches, achieving an accuracy of 98.37%, precision of 97.65%, recall of 98.02%, and F1-score of 97.83%. The low variability in performance across cross-validation folds indicates the model's robustness and generalizability. This research contributes a novel IoT traffic dataset and a cost-effective, adaptive anomaly detection framework capable of detecting subtle anomalies.
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Copyright (c) 2026 Praveen Gujjar, Raghavendra M Devadas, Nikitha K
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