Deep Learning-Based Network Traffic Anomaly Detection: A Study in IoT Environments
DOI:
https://doi.org/10.53469/wjimt.2024.07(06).03Keywords:
Internet of Things (IoT), Network Anomaly Detection, Deep Learning, LSTMAbstract
This study presents a deep learning technique to detect vulnerabilities in the Internet of Things (IoT) environment. The proposed method combines the manual design with the learning function of autoencoders, together with the deep neural network architecture associated with the Long Short-Term Memory (LSTM) layer. Experiments performed on the IoT-23 dataset show that our method outperforms traditional machine learning and state-of-the-art deep learning methods, achieving an accuracy of 99.2%, an F1-score of 0.987, and an AUC-ROC of 0.998. The framework addresses critical issues in IoT security, including device diversity, vehicle model diversity, and real-time research needs. The ablation studies show the importance of combining manual and autoencoder-based feature extraction. Grad-CAM visualisations improve the model definition by identifying the essential features for classifying bad vehicles and not good. The model's ability to capture the time-dependent nature of network flows makes it helpful in investigating complex, time-dependent variables. Although there are limitations in computing needs and general capabilities across IoT ecosystems, the proposed system shows significant potential for practical use in IoT security systems. This research contributes to advancing IoT security by providing a powerful, efficient, and easily interpretable system to discover the system's capabilities in terms of strengths and weaknesses in the IoT network environment.
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