Improved Transformer-Conv-BiLSTM-based method for intrusion detection

Abstract

Due to the continuous expansion of the Internet scale, network traffic has experienced an explosive growth, accompanied by increasingly complex structures. Improving the detection accuracy of malicious traffic and efficiently distinguishing different categories of malicious traffic has become an urgent problem to be addressed. Research has shown that hybrid approaches combining CNN and BiLSTM exhibit strong responsiveness and perform well in solving research problems such as video classification, sentiment analysis, and emotion recognition. Therefore, in order to enhance the learning capability and detection performance of IDS, this paper proposes an improved version of an intrusion detection method based on the Transformer and Conv-BiLSTM networks. This model combines the advantages of both modules to improve performance compared to traditional models.