Research on a lightweight real-time anomaly traffic detection method

Haijun Geng; Min Ren; Qingsheng Wang; Shuanglong Liang; Manlan Zhou

doi:10.1117/12.3025720

8 April 2024 Research on a lightweight real-time anomaly traffic detection method

Haijun Geng, Min Ren, Qingsheng Wang, Shuanglong Liang, Manlan Zhou

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130903Q (2024) https://doi.org/10.1117/12.3025720
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Anomalous traffic detection in the network is one of the essential components of network security protection. Neural networks are widely used in intrusion detection systems, which can learn the ability to distinguish between regular and attack traffic in the network through training. However, most of the existing methods do unimodal anomaly detection on temporal features. Based on this, our paper proposes a hybrid anomaly detection model based on Auto-Encoder, which uses Damped Incremental Statistics to extract features from the data to obtain Time-domain features, then uses Discrete Wavelet Transform to obtain Frequency-domain features of corresponding dimensions using two sets of Auto-Encoders to generate the RMSE in two modules and weighted sum to get the final RMSE value. The anomaly detection problem is transformed into a refactoring problem of the time series in two spaces. The classification algorithm compares the anomaly with the threshold value, thus determining whether it is an attacking traffic. The experimental results in UNSW-NB15, IDS2017, and IDS2018 datasets show that the method in paper is better than the traditional unimodal anomaly detection method, which improves 8.11% and 7.31% in Precision and Recall, respectively, and the Precision can be up to 99.99%, which proves the validity of the method in our paper.

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

Citation Download Citation

Haijun Geng, Min Ren, Qingsheng Wang, Shuanglong Liang, and Manlan Zhou "Research on a lightweight real-time anomaly traffic detection method", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130903Q (8 April 2024); https://doi.org/10.1117/12.3025720

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KEYWORDS

Data modeling

Discrete wavelet transforms

Feature extraction

Machine learning

Time-frequency analysis

Computer intrusion detection

Neural networks

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