Abstract
With the rapid development of the Internet, the methods of cyber attack have become more complex and the damage to the world has become increasingly greater. Therefore, timely detection of malicious behavior on the Internet has become an important security issue today. This paper proposes an intrusion detection system based on deep learning, applies bidirectional long short term memory architecture to the system, and uses the UNSW-NB15 data set for training and testing. Experimental tests show that the intrusion detection system can effectively detect the known or unknown malicious behavior of the network under the current network environment.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (Grant Nos. 61772550, 61572521, U1636114), National Cryptography Development Fund of China Under Grants No. MMJJ20170112, National Key Research and Development Program of China Under Grants No. 2017YFB0802000, the Natural Science Basic Research Plan in Shaanxi Province of china (Grant Nos. 2016JQ6037) and Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS201610).
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Chen, W., Yang, S., Wang, X.A., Zhang, W., Zhang, J. (2019). Network Malicious Behavior Detection Using Bidirectional LSTM. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_57
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DOI: https://doi.org/10.1007/978-3-319-93659-8_57
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