Network traffic anomaly detection based on attention mechanism with LSTM

ZhaoYong Yang; JiaYing Wang; JiaLi Luo

doi:10.1117/12.3024647

3 April 2024 Network traffic anomaly detection based on attention mechanism with LSTM

ZhaoYong Yang, JiaYing Wang, JiaLi Luo

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 1307813 (2024) https://doi.org/10.1117/12.3024647
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

Aiming at the problems of the high dimension of features, high complexity of feature processing, and low efficiency of model detection of traditional industrial control network traffic data in complex network environments, this study uses an abnormal network flow identification and detection method based on random forest (RF), multi-head attention (ATT) and long short-term memory (LSTM) network. Firstly, the random forest algorithm is used to calculate the importance score of flow characteristics, screen out important features, and eliminate redundant features. Then, LSTM is adopted to identify and detect abnormal flows. In order to evaluate the effectiveness and superiority of the model, the accuracy, precision, recall, and F1-score are used in this study to evaluate the model, and the model is compared with traditional machine learning methods including Naive Bayes, QDA, and KNN algorithms. The experimental results show that the overall accuracy of abnormal flow identification reaches 99% on the CIC-IDS-2017 public data set.

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

Citation Download Citation

ZhaoYong Yang, JiaYing Wang, and JiaLi Luo "Network traffic anomaly detection based on attention mechanism with LSTM", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 1307813 (3 April 2024); https://doi.org/10.1117/12.3024647

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