标题： 基于MFCC的鲁棒网络入侵检测的频谱特征提取 显示英文标题

标题： Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs

摘要： 物联网（IoT）网络的迅速扩展导致安全漏洞激增，强调了需要强大的异常检测和分类技术的重要性。 在本工作中，我们提出了一种新方法，通过利用梅频率倒谱系数（MFCC）和ResNet-18，一种在特征提取和基于图像的任务中表现出色的深度学习模型，来识别物联网网络流量中的异常。 可学习的MFCC能够实现自适应的频谱特征表示，比传统的固定MFCC更有效地捕捉网络流量中的时间模式。 我们证明了将原始信号转换为MFCC可以将数据映射到更高维空间，增强类别可分性，并实现更有效的多类分类。 我们的方法结合了MFCC的优势与ResNet-18的稳健特征提取能力，为异常检测提供了一个强大的框架。 所提出的模型在三个广泛使用的物联网入侵检测数据集上进行了评估：CICIoT2023、NSL-KDD和IoTID20。 实验结果突显了将自适应信号处理技术与深度学习架构相结合的潜力，以在异构物联网网络环境中实现稳健且可扩展的异常检测。 显示英文摘要

摘要： 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.