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7 March 2022 How to mimicking the construction of deep learning model for log anomaly detection
Jia Sun, Jianhui Zhang, Youjun Bu, Bo Chen, Xiangyu Lu, Surong Zhang
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Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121672W (2022) https://doi.org/10.1117/12.2628598
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China
Abstract
Log anomaly detection based on deep learning is one of the research hotspots in the field of computer security. It is foreseeable that the mimicry theory proposed by Academician Wu Jiangxing will further improve the detection capabilities of deep learning models, but will also bring high resource consumption and difficulty in application. Therefore, this paper proposes a mimic model construction method that uses the output of complex models as prior knowledge to train lightweight heterogeneous execution bodies and then integrates them. Finally, it is based on DPCNN and TextCNN as complex models and lightweight executions respectively. The experiment of the body structure mimic model proves that while reducing the number of parameters from millions to thousands, its detection accuracy and F1 value are only about 2% and 4% lower than the original model, which greatly retains the original model. The detection capability.
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Jia Sun, Jianhui Zhang, Youjun Bu, Bo Chen, Xiangyu Lu, and Surong Zhang "How to mimicking the construction of deep learning model for log anomaly detection", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121672W (7 March 2022); https://doi.org/10.1117/12.2628598
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KEYWORDS
Convolution
Convolutional neural networks
Computer security
Defense and security
Feature extraction
Statistical modeling
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