Abstract
Gated Recurrent Unit (GRU) is a variant of a recurrent neural network, just like an LSTM network. Compared with RNN, the two networks have higher accuracy in processing sequence problems, and both of them have been proven to be effective in varieties of machine learning tasks such as natural language processing, text classification and speech recognition. In addition, the network unit structure of the GRU is simpler than the LSTM unit structure, which is more conducive to the training of the model. NSL-KDD datasets, which is the replacement of KDD cup 99, is still one of the datasets for measuring the effectiveness of intrusion detection models. In order to reduce the feature data dimension and combine the prior knowledge of computer network, a GRU intrusion detection method based on salient features (SF-GRU) is proposed. SF-GRU selects the distinctive features of response for different intrusion forms, and uses GRU network to identify the selected features to improve the efficiency of model detection. The experimental results show that compared with the traditional deep learning method, this proposal has higher accuracy and computational efficiency.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (61772321), CERNET Innovation Project (NGII20170508), and in part by Guangdong Province Key Research and Development Plan (Grant No. 2019B010137004), the National Key research and Development Plan (Grant No. 2018YFB1800701, No. 2018YFB0803504, and No. 2018YEB1004003), and the National Natural Science Foundation of China (Grant No. U1636215 and 61572492).
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Hou, J., Liu, F., Zhuang, X. (2019). A New Intrusion Detection Model Based on GRU and Salient Feature Approach. In: Wang, G., Bhuiyan, M.Z.A., De Capitani di Vimercati, S., Ren, Y. (eds) Dependability in Sensor, Cloud, and Big Data Systems and Applications. DependSys 2019. Communications in Computer and Information Science, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-15-1304-6_32
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DOI: https://doi.org/10.1007/978-981-15-1304-6_32
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