Open AccessNetwork intrusion detection method based on deep learning
Author(s) -
Shuai Zou,
Fangwei Zhong,
Bing Han,
Hao Sun,
Tao Qian,
Changjiang Yu,
Jia Jia
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1966/1/012051
Subject(s) - encryption , computer science , computer network , identification (biology) , network security , intrusion detection system , transmission (telecommunications) , computer security , data mining , artificial intelligence , telecommunications , botany , biology
With the rapid development of the network, network transmission encryption technologies such as SSL and SSH have emerged. Network traffic has grown exponentially, and transmission encryption has become an important means of protecting data security and privacy. However, encrypted data also brings hidden dangers that are not easily detectable to network security. Identifying the encrypted network traffic can effectively solve this problem. However, the current recognition probability is not high enough and the time delay caused by the recognition together makes it impossible to accurately detect and warn the network traffic. An encrypted network traffic recognition method based on deep learning is proposed. Experimental verification shows that the method is applied in the network. The accuracy of encrypted network traffic identification is 97.02%, which can meet actual needs.