Research on Network Intrusion Detection Based on Transformer

Gang Gan; Weiju Kong

doi:10.54097/fcis.v3i3.7987

Authors

Gang Gan
Weiju Kong

DOI:

https://doi.org/10.54097/fcis.v3i3.7987

Keywords:

Intrusion Detection, Deep Learning, Transformer, Bidirectional Long Short-Term Memory

Abstract

With the advancement of technology, the development of various industries has become inseparable from informatization. People's lives have become closely related to the network. While using the network to facilitate our lives, massive data is also generated. Traditional firewall technologies are no longer sufficient to meet current needs. Deep learning algorithms can establish complex mapping relationships between network data, and can extract hidden correlation features between data features to achieve data recognition and prediction. Therefore, this paper introduces Transformer and Bidirectional Long Short-Term Memory (BiLSTM) into the field of intrusion detection, and proposes an intrusion detection method based on the combination of Transformer-Encoder and BiLSTM (TBL). Deep Neural Networks (DNN) are used to further extract data features, and the softmax function is used to output classification results. In order to verify the effectiveness of this method, this paper trains and tests the TBL method on the NSL-KDD dataset, and verifies its feasibility and superiority.

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