Encrypted malicious traffic detection based on neural network

With the widespread application of encrypted communications, traditional malicious traffic detection methods based on content analysis have gradually become ineffective. How to efficiently detect malicious behavior in encrypted traffic has become a research focus in the field of network security. This paper proposes a neural network-based encrypted malicious traffic detection method, which realizes the classification of malicious encrypted traffic through a deep learning model. First, the network traffic is preprocessed and key features are extracted, including packet size distribution, time interval, and protocol type. The features are then mapped into a two-dimensional feature map as the input of the deep learning model. A scalable window self-attention mechanism is designed, and the Transfomer neural network model is used to classify feature maps, achieving efficient detection of malicious traffic.Experimental results show that this method performs well in detection accuracy, recall rate, and model robustness, and provides a feasible solution to the problem of malicious behavior detection in encrypted traffic.