A Malicious Domain Detection Model Based on Improved Deep Learning. (25th June 2022)

• Record Type: Journal Article
• Authors: Huang, XiangDong; Li, Hao; Liu, Jiajia; Liu, FengChun; Wang, Jian; Xie, BaoShan; Chen, BaoPing; Zhang, Qi; Xue, Tao
• Other Names: Mumtaz Shahid Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-06-25
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/9241670
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : With the rapid development of the Internet, malicious domain names pose more and more serious threats to many fields, such as network security and social security, and there have been many research results on malicious domain detection. This article proposes a malicious domain name detection model based on improved deep learning, which can combine the advantages of three different network models, convolutional neural network (CNN), temporal convolutional network (TCN), and long short-term memory network (LSTM) in malicious domain name detection, to obtain a better detection effect than that of the original single or two models. Experiments show that the effect of the improved deep learning model proposed in this article is better than that of the combined model of CNN and LSTM or the combined model of CNN and TCN, and the accuracy and regression rates reached 99.76% and 98.81%, respectively.