Domain texts usually have significant domain features and long text lengths. Text classification models suitable for general fields cannot well meet the task of text classification in specific domains. Therefore, this paper proposes a domain text classification method based on BERT. First, segment the long domain text to obtain the sequence combination, and input it into the BERT pre-training model to obtain the word vector. Then the vector is compressed and encoded to obtain the pooled sequence feature vector. Finally, it is sequentially input to the Encoder layer for domain feature extraction, and the text is divided into various categories in the output layer. The experimental results show that the proposed BERT_VCA model has an average improvement of 1.12% in F1 value compared with the BERT_BASE model in the domain text classification task.
With the development of social science and technology, the communication process is often accompanied by a variety of encrypted malicious traffic intrusion into the network system. Therefore, it is necessary to build a malicious traffic detection system to solve this problem. This paper proposes a construction scheme of malicious traffic detection system based on machine learning. The malicious traffic data selects the public cic-ids-2017 dataset, and carries out feature engineering processing on the traffic data, which is transformed into a data feature set that is easy to be trained by machine learning model. The performance of various machine learning models are tested and compared. Finally, the optimal random forest model is selected as the malicious traffic detection model. Experiment shows that the model can effectively detect encrypted malicious traffic.
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