Abstract

The exponential growth of Enhanced Machine-Type Communication (eMTC) in 5G and beyond networks is fundamental to the evolving Internet of Things (IoT) landscape, connecting billions of low-power devices. This rapid growth, while transformative, significantly broadens the attack surface, introducing critical security challenges that traditional measures struggle to address. This study proposes an advanced anomaly detection framework leveraging Convolutional Autoencoders (Conv AEs) to identify unusual patterns and potential security threats in eMTC-enabled IoT data traffic. The Conv AE model was developed and rigorously evaluated using the CICIoT2023 dataset, which comprises over 46 million records of both normal and anomalous network traffic. Through binary stratified sampling and z-score normalization, a balanced and representative dataset was prepared for training and evaluation. The model was trained exclusively on normal traffic to learn benign behavior. The Conv AE architecture, comprising an encoder with 1D convolutional layers and a decoder with 1D transposed convolutions, optimizes a Mean Squared Error (MSE) loss function to reconstruct input data, flagging anomalies based on high reconstruction errors exceeding an empirically selected threshold of 0.0262. The evaluation metrics demonstrate the framework's exceptional effectiveness and efficiency. The model achieved an impressive Accuracy of 0.9892, a Precision of 0.9987, a Recall (True Positive Rate) of 0.9903, and an F1-Score of 0.9945. These results indicate a strong ability to detect anomalous events with remarkably few false positives. Furthermore, the model exhibited efficient operational performance, with a per-sample inference time of 0.0809 seconds and the ability to process a full test set of 102,578 samples in 52.87 seconds, enabling real-time anomaly detection. The False Alarm Rate (FAR) was 0.0530, and the True Negative Rate (TNR) was 0.9470, confirming the model's proficiency in distinguishing normal from malicious traffic. The high Kappa Score of 0.8000, Matthews Correlation Coefficient (MCC) of 0.8096, Geometric Mean (G-Mean) of 0.9684, AUC-ROC of 0.9895, and AUC-PR of 0.9996 further validate the model's balanced and robust performance. This research contributes a robust and efficient solution for enhancing the security and safe deployment of IoT systems within the eMTC framework on current and future generation networks. Future research may adapt this binary framework to a multi-class classification approach, enabling classification of attack types for more detailed threat insight in eMTC-enabled IoT applications on 5G and beyond networks.

Key words: Network Anomaly Detection, Enhanced Machine-Type Communication (eMTC), 5G IoT Security, Convolutional Autoencoder (Conv AE), Deep Learning (DL), Real-Time Threat Detection.