A malicious traffic detection algorithm based on the combination of traffic statistical feature and BERT text feature

Nowadays, most malicious traffic detection algorithms are based on statistical characteristics for analysis. However, as the behaviors of malicious traffic are constantly evolving, attackers are constantly refining their techniques to evade these statistical feature-based detection algorithms. In today's complex network environment, relying solely on statistical feature to detect malicious traffic may not be able to identify all malicious traffic. Therefore, this paper proposes a clas-sification detection method that integrates statistical feature with BERT text fea-ture as the training and testing features of the classification model. The classifica-tion model utilizes variational autoencoders to capture malicious traffic's latent patterns and anomalous features. Experimental results show that the proposed method in this paper can classify malicious traffic with an accuracy of 99 , which is significantly better than other malicious traffic detection algorithms. The proposed method combines mixed features with probabilistic modeling, signifi-cantly improving the accuracy of detecting malicious traffic, and enabling early detection and prevention of potential network attacks and threats.