@inproceedings{18079a9bcdde4bf99cbc5e99f4fede23,
title = "Network Attack Detection Method Based on Convolutional Neural Network",
abstract = "The existing traditional network attack detection algorithms have low detection accuracy and are difficult to cope with external malicious and capricious network attacks in the communication and control systems. In order to solve this problem, this paper designs a network attack detection method based on convolutional neural network technology, which improves the recognition accuracy of attack detection for network attack signals. Specifically, a convolutional neural network structure with overlapping pooling layers is designed, and the improved data preprocessing method and parameter tuning make the recognition accuracy of the obtained convolution network on the KDD99 data set greatly improved. Finally, the experimental results show that the proposed network tuning strategy is effective, and the neural network structure designed in this paper is superior to other existing intelligent detection algorithms in terms of recognition accuracy.",
keywords = "Attack detection system, Convolutional neural network, Deep learning, Network environment",
author = "Nan Ding and Yang Liu and Yimin Fan and Dangyang Jie",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2019 ; Conference date: 26-10-2019 Through 27-10-2019",
year = "2020",
doi = "10.1007/978-981-32-9686-2\_68",
language = "英语",
isbn = "9789813296855",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "610--620",
editor = "Yingmin Jia and Junping Du and Weicun Zhang",
booktitle = "Proceedings of 2019 Chinese Intelligent Systems Conference - Volume II",
address = "德国",
}