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A Multi-Scale Feature Extraction Method Based on Improved Transformer for Intrusion Detection

Year: 2024

Type: article

Abstract: Network traffic is a crucial indicator of network performance and network intrusions typically result in traffic anomalies. Capturing the differences and commonalities between different input features is challenging due to high-dimensional traffic data. To address this, we propose a multi-scale feature extraction method based on global additive attention (MSFE-GAA), which integrates time position information encoded by trigonometric functions to capture multi-scale temporal features. An improved Transformer with a similarity matrix captures the commonalities and differences, enhanced by global additive attention for long-term dependencies. Experiments on two public datasets show that the MSFE-GAA model outperforms other baseline models.

Source: International Journal of Information Security and Privacy

DOI: 10.4018/ijisp.356893

Authors: Lijun Gu, Zhongye Wang

Institutions: Changzhou Institute of Mechatronic Technology, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4403388629

FWCI:

Cites: 34

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Related to: 10