DDoS network attack detection technology based on double-stacked LSTM

Jie Li; XiaoSong Zhang; JunKai Yi; RuiRong Chen

doi:10.1117/12.2641266

4 August 2022 DDoS network attack detection technology based on double-stacked LSTM

Jie Li, XiaoSong Zhang, JunKai Yi, RuiRong Chen

Proceedings Volume 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022); 123060S (2022) https://doi.org/10.1117/12.2641266
Event: Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 2022, Changchun, China

Abstract

In the current big data environment, aiming at the problems that traditional machine learning needs manual intervention and time-consuming to detect DDoS attacks, a DDoS attack detection method based on a double-stacked long short-term memory network is presented. The preprocessed data stream is sorted by recursive feature elimination algorithm, and the features with the most DDoS attack characteristics are selected as high-quality features, forming a double-stacked long short-term memory network data input format. The Center Loss is introduced into the Softmax Loss to reduce the intraclass distance, further improve the classification accuracy. Finally, the information containing DDoS attack characteristics can be quickly extracted from the complex characteristics of traffic. The CIC-IDS2017 dataset is used to train the model. Experimental research shows that the proposed model has an accuracy rate of 99.48% compared with other neural network models, and the detection effect is better than the compared algorithms, which can effectively achieve DDoS attack detection.

Citation Download Citation

Jie Li, XiaoSong Zhang, JunKai Yi, and RuiRong Chen "DDoS network attack detection technology based on double-stacked LSTM", Proc. SPIE 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 123060S (4 August 2022); https://doi.org/10.1117/12.2641266

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KEYWORDS

Data modeling

Performance modeling

Neural networks

Machine learning

Neurons

Visual process modeling

Visualization

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