FREDet: Fine-Grained Malicious Traffic Detection Based on Frequency Domain Features

Machine learning methods have shown significant advantages in detecting malicious traffic, particularly identifying zero-day attacks and unknown threats. However, existing detection methods based on statistical features are susceptible to deception and evasion by attackers, resulting in reduced detection accuracy and challenges in achieving fine-grained detection. To address these issues, we propose FREDet, a fine-grained malicious traffic detection method based on frequency domain features. FREDet employs discrete wavelet transform to extract frequency domain features from network traffic and incorporates a new traffic aggregation method, significantly enhancing feature representativeness and detection accuracy for robust, fine-grained detection. Experiments on a multi-type attack dataset demonstrate that FREDet can detect malicious traffic with high precision, outperforming existing state-of-the-art methods, achieving accuracies of over 98.63% and 99.93% in detecting fine-grained attack categories and known attack subcategories, respectively.