Network Intrusion Detection based on LSTM

Huiqun Zeng; Huiqian Chen

doi:10.54691/p4w71z56

Network Intrusion Detection based on LSTM

Huiqun Zeng, Huiqian Chen

Frontiers in Science and Engineering, (2024), Vol.4, No.9, pp.131-137

Published: September 20, 2024

DOI: 10.54691/p4w71z56

PDF: Download Full Text PDF

Abstract

Network intrusion detection, as an important means of ensuring daily network security, its accuracy and response speed are crucial for defending against network attacks. This article explores and implements deep learning based network intrusion detection techniques, particularly the application of Long Short Term Memory (LSTM) networks in detecting network intrusion behavior. The aim is to solve the problems of gradient vanishing and exploding in traditional RNNs, improve the emergency response capability of network systems, and enhance the reliability and security of networks. The study used the KDD99 dataset to demonstrate the effectiveness of the LSTM model in network intrusion detection. The experimental results show that the constructed LSTM model achieves an accuracy of 88.01% in network intrusion detection tasks, demonstrating high accuracy and feasibility.

Keywords: Network Intrusion Detection; Deep Learning; LSTM.

APA Citation: Huiqun Zeng, Huiqian Chen (2024). Network Intrusion Detection based on LSTM. Frontiers in Science and Engineering, 4(9), 131-137. https://doi.org/10.54691/p4w71z56

References

YAO R, LIU C, ZHANG L, et al. Unsupervised anomaly detection using variational auto-encoder based feature extraction[C]//2019 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2019: 1-7.
ZHANG Y, PENG P, LIU C, et al. Anomaly detection for industry product quality inspection based on gaussian restricted boltzmann machine[C]//2019 IEEE international conference on systems, man and cybernetics (SMC). IEEE, 2019: 1-6.
AGARAP A F M. A neural network architecture combining gated recurrent unit (gru) and support vector machine (svm) for intrusion detection in network traffic data[C]//Proceedings of the 2018 10th international conference on machine learning and computing. 2018: 26-30.
HE K, SUN J. Convolutional neural networks at constrained time cost[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 5353-5360.
Zhang, H., Xu, W., Wang, X., & Mao, S. (2020). LSTM-based intrusion detection model with attention mechanism for industrial control systems. IEEE Transactions on Industrial Informatics, 16(6), 4087-4097.
Li, J., Yan, Z., Zhang, X., & Cao, Z. (2017). A deep learning approach for user-aware service-based network traffic classification. IEEE Transactions on Industrial Informatics, 13(6), 3134-3143.
Zekri, M., & Benferhat, S. (2018). Comparison study of recurrent neural networks for botnet detection. Procedia Computer Science, 127, 109-118.
Sgandurra, D., Conti, M., & Dragoni, N. (2018). DeeplearningIDS: A dataset for deep learning-based intrusion detection in SCADA systems. Data in brief, 19, 2214-2219.