用可学习的梅尔倒谱系数提升物联网网络异常检测能力
Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
- 用可学习的MFCC提取网络流量的频谱特征，适应性强
- 在三个数据集上准确率超95%，优于传统方法
- 适合做物联网安全防护的工程师和研究人员
物联网（IoT）网络的快速扩展带来了大量安全漏洞，亟需鲁棒的异常检测与分类技术。本文提出一种新方法，利用梅尔频率倒谱系数（MFCC）与ResNet-18模型进行物联网网络流量异常识别。可学习的MFCC实现自适应频谱特征表示，比传统固定MFCC更有效捕捉流量的时间模式。将原始信号转换为MFCC能映射到高维空间，增强类别可分性，支持更优的多类分类。该方法结合了MFCC的时频特性与ResNet-18强大的特征提取能力，构建出高效异常检测框架。在CICIoT2023、NSL-KDD和IoTID20三个主流物联网入侵检测数据集上评估，结果表明融合自适应信号处理与深度学习架构，可在异构物联网环境中实现鲁棒且可扩展的异常检测。
原文摘要 · Abstract (English)
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.
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