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A Malicious Domain Detection Model Based on Improved Deep Learning.
Huang, XiangDong; Li, Hao; Liu, Jiajia; Liu, FengChun; Wang, Jian; Xie, BaoShan; Chen, BaoPing; Zhang, Qi; Xue, Tao.
Afiliação
  • Huang X; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei, China.
  • Li H; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, China.
  • Liu J; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, China.
  • Liu F; College of Science, North China University of Science and Technology, Tangshan, Hebei, China.
  • Wang J; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, China.
  • Xie B; Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, Hebei, China.
  • Chen B; College of Science, North China University of Science and Technology, Tangshan, Hebei, China.
  • Zhang Q; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei, China.
  • Xue T; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, China.
Comput Intell Neurosci ; 2022: 9241670, 2022.
Article em En | MEDLINE | ID: mdl-35795747
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article