Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
This work addresses security vulnerabilities in IoT networks by improving anomaly detection, though it appears incremental as it combines existing techniques (MFCCs and ResNet-18) in a new application.
The paper tackled the problem of detecting anomalies in IoT network traffic by proposing a novel approach using learnable Mel-frequency cepstral coefficients (MFCCs) and ResNet-18, achieving robust multiclass classification on datasets like CICIoT2023, NSL-KDD, and IoTID20.
The rapid expansion of Internet of Things (IoT) networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging the Mel-frequency cepstral coefficients (MFCC) and ResNet-18, a deep learning model known for its effectiveness in feature extraction and image-based tasks. Learnable MFCCs enable adaptive spectral feature representation, capturing the temporal patterns inherent in network traffic more effectively than traditional fixed MFCCs. We demonstrate that transforming raw signals into MFCCs maps the data into a higher-dimensional space, enhancing class separability and enabling more effective multiclass classification. Our approach combines the strengths of MFCCs with the robust feature extraction capabilities of ResNet-18, offering a powerful framework for anomaly detection. The proposed model is evaluated on three widely used IoT intrusion detection datasets: CICIoT2023, NSL-KDD, and IoTID20. The experimental results highlight the potential of integrating adaptive signal processing techniques with deep learning architectures to achieve robust and scalable anomaly detection in heterogeneous IoT network landscapes.