Abstract
Network threats are malicious attacks that endanger network security. With terabits of information stored in the network and much of this information being confidential, cyber security turns to be very important. Most network protection mechanisms are based on firewall and Intrusion Detection System (IDS). However, with the diversification of cyber-attacks, traditional defense mechanisms cannot fully guarantee the security of the network. In this paper, we propose an automatic network threat response system based on machine learning and deep learning. It comprises three sub-modules: threat detection module, threat identification module and threat mitigation module. The experimental results show that the proposed system can handle 22 types of network threats in the KDD99 dataset and the rate of successful response is over 97%, which is much better than the traditional ways.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (No.61728303) and the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT1800417); this work is also supported by China NSFC 61836005 and 61672358.
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Xia, S., Qiu, M., Liu, M., Zhong, M., Zhao, H. (2019). AI Enhanced Automatic Response System for Resisting Network Threats. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2019. Lecture Notes in Computer Science(), vol 11910. Springer, Cham. https://doi.org/10.1007/978-3-030-34139-8_22
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DOI: https://doi.org/10.1007/978-3-030-34139-8_22
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