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5 May 2022 Combined with the dual-attention and the long short-term memory intrusion detection research
Shuo Lin, Shengnan Xing
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Proceedings Volume 12245, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022); 122450D (2022) https://doi.org/10.1117/12.2635864
Event: International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), 2022, Sanya, China
Abstract
Aiming at the problem that traditional deep learning methods have low detection rate of hidden attacks when processing high-dimensional data, an intrusion detection model based on recurrent neural network is proposed. Use the Borderline-Smote algorithm to oversample the data set, On the basis of LSTM, double-layer attention mechanism is introduced to extract the main feature information through byte and sequence dimensions to generate a more accurate feature representation. Finally, the softmax classifier is used to obtain the classification results. Use UNSW-NB15 data set as the experimental data, the experimental results show that this model is compared with the similar deep learning detection model has higher accuracy and lower the rate of false positives.
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Shuo Lin and Shengnan Xing "Combined with the dual-attention and the long short-term memory intrusion detection research", Proc. SPIE 12245, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), 122450D (5 May 2022); https://doi.org/10.1117/12.2635864
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KEYWORDS
Data modeling
Computer intrusion detection
Data storage
Statistical modeling
Network security
Neural networks
Data processing
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