IWO Optimization SKohonen Network in the Application of Detecting Malicious Domain Name

• Published in: 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS) pp. 67 - 70
• Main Authors: Huang, Jingyu, Zhang, Guidong, Shen, Yongjun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.10.2020
• Subjects: Classification algorithms; component; IWO algorithm; malicious domain name detection; network security; Neural networks; Prediction algorithms; Privacy; Security; SKohonen neural network; Software algorithms; Software engineering
• ISBN: 1728165784; 9781728165783
• ISSN: 2327-0594
• DOI: 10.1109/ICSESS49938.2020.9237630

As one of the main ways to destroy network security, malicious domain name attack often brings economic loss and privacy leakage to enterprises and users. In this paper, a malicious domain name detection model is proposed to optimize SKohonen network based on IWO algorithm. The location information of weed individuals with the smallest fitness value is generated by IWO algorithm as the optimal solution of IWO algorithm. The optimal solution is set as the initial weight vector parameter of SKohonen neural network, and the domain name data set is predicted and classified. Then it is compared with the malicious domain name detection model of SKohonen neural network, and measured by classification histogram, confusion matrix, ROC curve and AUC value. The results show that the malicious domain name detection model based on IWO algorithm to optimize SKohonen network is better for the classification of malicious domain names. In the prevention of malicious domain name attack direction, has high practical value.(Abstract)