Hidden in Time, Revealed in Frequency: Spectral Features and Multiresolution Analysis for Encrypted Internet Traffic Classification

Nathan Dillbary, Roi Yozevitch, Amit Dvir, Ran Dubin, Chen Hajaj

Abstract: In recent years, privacy and security concerns have led to the wide adoption of encrypted protocols, making encrypted traffic a major portion of overall communications online. The transition into more secure protocols poses significant challenges for internet service providers to utilize traditional traffic classification techniques in order to guarantee the Quality of Service (QoS), Quality of Experience (QoE), and cyber-security of their customers. In this work, we introduce two methods, namely STFT-TC and DWT-TC, leveraging compact time-series representation coupled with well-known techniques from the field of Digital Signal Processing (DSP): the short-time Fourier transform (STFT) and the discrete wavelet transform (DWT). The STFT-TC method extracts a suite of statistical and spectral features from the magnitude spectrogram, offering a fresh perspective on interpreting and classifying encrypted traffic. The DWT-TC method extracts statistical features from the wavelet coefficients and incorporates unique characteristics that describe the signal's shape and energy distribution. Evaluating our methods on two public QUIC datasets demonstrated improvements in accuracy of up to 5.7%. Similarly, the F1-scores also showed enhancements, with increments of up to 5.9% for the same datasets.