A Malicious Domain Detection Model Based on Improved Deep Learning

Autor:	XiangDong Huang, Hao Li, Jiajia Liu, FengChun Liu, Jian Wang, BaoShan Xie, BaoPing Chen, Qi Zhang, Tao Xue
Rok vydání:	2022
Předmět:	Internet Deep Learning Memory Long-Term Article Subject General Computer Science General Mathematics General Neuroscience Neural Networks Computer General Medicine
Zdroj:	Computational Intelligence and Neuroscience. 2022:1-13
ISSN:	1687-5273 1687-5265
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e87f531c5ae38b6af32c232ba4a095b8 https://doi.org/10.1155/2022/9241670 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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