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26 June 2023 Anomaly detection of terminal access based on LSTM and ResNet
Nige Li, Yong Li, Ziang Lu, Duanchao Li, Jinjin Ding
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Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 1272103 (2023) https://doi.org/10.1117/12.2683553
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China
Abstract
Massive wireless debugging terminals and complex and diverse access requirements pose significant challenges to the secure access of substation terminal equipment. It is crucial to detect anomaly network traffic to ensure the security of terminal access to the substation. At present, network traffic anomaly detection based on traditional deep learning often has the problem of low computational efficiency or weak representation ability. Given the low computational efficiency of traditional deep learning, the residual network is used to extract spatial features of data, which can effectively improve convergence speed and time efficiency. Aiming at the problem of weak representation ability of traditional machine learning methods, the long short-term memory network (LSTM) is used to improve the representation ability to learn while learning the temporal characteristics of traffic and prevent the gradient from disappearing and network degradation. Experimental results show that compared with the traditional deep learning method, the accuracy of the proposed method is improved, the F1 score reaches 90.09, and the AUC is up to 0.981. By improving anomaly detection accuracy, the paper further guarantees terminal security.
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Nige Li, Yong Li, Ziang Lu, Duanchao Li, and Jinjin Ding "Anomaly detection of terminal access based on LSTM and ResNet", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 1272103 (26 June 2023); https://doi.org/10.1117/12.2683553
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KEYWORDS
Data modeling
Deep learning
Feature extraction
Network security
Convolution
Neural networks
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