Learning long- and short-term dependencies for network intrusion detection

Qionglan Na; Shijun Zhang; Xin Li; Yixi Yang; Ji Lai; Jing Zeng

doi:10.1117/12.3034217

16 October 2024 Learning long- and short-term dependencies for network intrusion detection

Qionglan Na, Shijun Zhang, Xin Li, Yixi Yang, Ji Lai, Jing Zeng

Author Affiliations +

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 1329148 (2024) https://doi.org/10.1117/12.3034217
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

Accurate network intrusion detection is very important for the security of power information system. In this study, we introduce an innovative architecture designed for network intrusion detection to enhance the security of power grid information systems. Our proposed framework integrates the WaveNet and BiLSTM models to effectively capture both short-term and long-term sequence dependencies present in intrusion traffic. The WaveNet model is employed to capture short-term dependencies by effectively modeling local patterns. In contrast, the BiLSTM model is utilized to capture long-term dependencies by excelling in capturing broader patterns across extended sequences. This combination of models enables a comprehensive understanding of the complex temporal dependencies exhibited in intrusion traffic data. To further enhance the model's performance, hybrid pooling layers are incorporated, comprising both maximum pooling and average pooling layers. This combination enables the capture of both global and local features, resulting in a more comprehensive data representation. The proposed model is rigorously evaluated using multiple datasets, demonstrating competitive intrusion recognition accuracy. These results emphasize the model's effectiveness in safeguarding the security of power grid information systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qionglan Na, Shijun Zhang, Xin Li, Yixi Yang, Ji Lai, and Jing Zeng "Learning long- and short-term dependencies for network intrusion detection", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 1329148 (16 October 2024); https://doi.org/10.1117/12.3034217

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KEYWORDS

Computer intrusion detection

Data modeling

Machine learning

Network security

Information security

Deep learning

Network architectures

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