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While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT) model. At the byte-level granularity, we initially employ the Bidirectional Gated Recurrent Unit (BiGRU) model to extract temporal features from bytes, followed by the utilization of the Text Convolutional Neural Network (TextCNN) model with multi-sized convolution kernels to extract local multi-receptive field spatial features. The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic. Our approach achieves accuracy and F1-score of 99.39% and 99.40%, respectively, on the publicly available USTC-TFC2016 dataset, and effectively reduces sample confusion within the Neris and Virut categories. The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

BSTFNet: An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features