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18 November 2024 Intrusion detection based on the SS-CBLM model
Tinghui Huang, Wenshuai Zhang, Yu Wang
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Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032B (2024) https://doi.org/10.1117/12.3051336
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China
Abstract
To address the issue of low intrusion detection accuracy due to incomplete feature extraction by a single model, an SSCBLM model detection method is proposed. This method combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) to extract and integrate features. SE modules and self-attention mechanisms are incorporated into the CNN and Bi-LSTM, respectively, to enhance the model's ability to extract important features. Finally, a Multilayer Perceptron (MLP) model is used as a classifier for multi-classification. Additionally, class merging, RandomUnderSampler, and Synthetic Minority Over-sampling Technique (SMOTE) are employed to address the class imbalance issue. The model is trained on the CIC-IDS2017 dataset, and experimental results show that the model achieves an accuracy of 99.94%, effectively detecting abnormal traffic.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tinghui Huang, Wenshuai Zhang, and Yu Wang "Intrusion detection based on the SS-CBLM model", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032B (18 November 2024); https://doi.org/10.1117/12.3051336
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KEYWORDS
Computer intrusion detection
Feature extraction
Convolutional neural networks
Feature selection
Network security
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