Open AccessIntrusion Detection of Vehicle Based on Generative Adversarial Networks
Author(s) -
Ying Jiang Liu,
Juan Wang,
Yang Zhao
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 - 10.1088/1742-6596/1757/1/012052
Subject(s) - computer science , intrusion detection system , adversarial system , data mining , process (computing) , generative grammar , overhead (engineering) , data set , artificial intelligence , field (mathematics) , machine learning , set (abstract data type) , generative adversarial network , training set , deep learning , mathematics , pure mathematics , programming language , operating system
In the process of applying deep learning to intrusion detection, in order to ensure the recognition accuracy of the model, a large number of data sets need to be classified manually, and then the model training is carried out after labeling. In practice, the efficiency of manual label designation for enough data sets is extremely low. This paper aims at the experimental data set encountered in the intrusion detection of intelligent network vehicles The problem of classification difficulty is proposed, and a method of vehicle intrusion detection based on Generative Adversarial Networks is proposed. Firstly, the vehicle driving data is collected, the collected data are put into the Generative Adversarial Networks for data classification, and the data set after training classification is used for model training. The experimental results show that the data classification can effectively improve work efficiency and reduce the resource overhead, which is practical in the application field.