Research Article
Research on Automatic Defense Network Active Attack Data Location and Early Warning Method
@INPROCEEDINGS{10.1007/978-3-030-67871-5_24, author={Jian-zhong Huang and Wen-da Xie}, title={Research on Automatic Defense Network Active Attack Data Location and Early Warning Method}, proceedings={Advanced Hybrid Information Processing. 4th EAI International Conference, ADHIP 2020, Binzhou, China, September 26-27, 2020, Proceedings, Part I}, proceedings_a={ADHIP}, year={2021}, month={2}, keywords={Automatic defense Network Active attack data Location Early warning}, doi={10.1007/978-3-030-67871-5_24} }- Jian-zhong Huang
Wen-da Xie
Year: 2021
Research on Automatic Defense Network Active Attack Data Location and Early Warning Method
ADHIP
Springer
DOI: 10.1007/978-3-030-67871-5_24
Abstract
The traditional automatic defense network active attack data location and early warning method has the shortcoming of poor localization performance, so the research of automatic defense network active attack data location and warning method is put forward. The active attack data is detected by the space distance of the network node data, and the active attack data is judged whether there is active attack data in the network, which is based on the detected active attack data. The multi-objective binary particle swarm optimization (BPSO) algorithm is used to obtain the optimal task allocation scheme for active attack data location. Based on it, the algorithm of extreme learning machine is used to realize the location and early warning of active attack data. Through the experiment, put forward the automatic Compared with traditional methods, the convergence value of active attack data location and early warning method of defensive network increases 23.61 and the error rate of location decreases by 15. It is fully explained that the proposed automatic defense network active attack data location and early warning method has better localization performance.
