Monitoring and early warning of new cyber-telecom crime platform based on BERT migration learning

The network is a major platform for implementing new cyber-telecom crimes. Therefore, it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms, which will lay the foundation for the establishment of prevention and control systems to protect citizens' property. However, the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks. For instance, the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling. Therefore, a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed. This method first identifies the text data and their tags, and then performs migration training based on a pre-training model. Finally, the method uses the fine-tuned model to predict and classify new cyber-telecom crimes. Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method, compared with the deep-learning method.