Abstract:

As one of the main means of Internet attack, malicious domain name brings huge network use risk to users and enterprises. In order to resist the attack of malicious domain names more effectively and ensure the security of cyberspace, this paper proposes a malicious domain name detection model based on thought evolution algorithm to optimize S-Kohonen neural network. This model using Kohonen neural network, and in the hidden layer after adding an additional output layer, the improvement for supervised neural network S-Kohonen, make its better learning characteristics of malicious domain name, related recycle mind evolutionary algorithm, the initial weights and threshold of neural network are optimized, finally it is concluded that the model can quickly and accurately detect the malicious domain name. Through MATLAB simulation of the model, and the mind evolutionary algorithm to optimize the BP neural network, from the confusion matrix, classification of histogram, ROC curve and AUC value in the form of specific analysis of the classification of the two models, the results show that the classification model for malicious domain with high accuracy, fast identification characteristics, can be used in the malicious domain of network security protection, and have higher practical value.

Key words: S-Kohonen neural network, supervised learning, evolutionary thinking algorithms, malicious domain name detection