中圖分類號： TN915.08 文獻標(biāo)識碼： A DOI：10.16157/j.issn.0258-7998.222843 中文引用格式： 黃學(xué)臻,，翟翟，周琳,，等. 基于輕量級密集神經(jīng)網(wǎng)絡(luò)的車載自組網(wǎng)入侵檢測方法[J].電子技術(shù)應(yīng)用,，2022，48(7)：67-73. 英文引用格式： Huang Xuezhen,，Zhai Di,，Zhou Lin，et al. Intrusion detection method for VANET based on light dense neural network[J]. Application of Electronic Technique,，2022,，48(7)：67-73.
Intrusion detection method for VANET based on light dense neural network
Huang Xuezhen1，Zhai Di2,，Zhou Lin2,，Zhu Yaru2
1.The First Research Institution of Ministry of Public Security of PRC，Beijing 100044,，China,； 2.Security and Privacy in Intelligent Transportation，Beijing Jiaotong University,，Beijing 100044,，China
Abstract： In the vehicular ad-hoc network, attackers can publish false traffic information by forging or tampering with messages, etc., resulting in traffic congestion or even serious traffic accidents. However, traditional intrusion detection methods cannot meet the application requirements of vehicular ad-hoc network. In order to solve the problems such as low performance, instability and high storage and time cost of intrusion detection methods in the current vehicular ad-hoc network, this paper proposes an intrusion detection method L-DenseNet(Light Dense Neural Network) based on dense neural network. The L-DenseNet is proposed to reduce the complexity of the model and improve the training speed and deployment adaptability of the detection algorithm. The proposed method is more suitable for intrusion detection in vehicle ad hoc networks. This paper conducts comparative experiments on the VeReMi dataset. The results show that the method proposed has the best overall performance in identifying various types of attacks in terms of precision and recall. As the same time, this method has less time cost and storage overhead.
Key words : vehicular ad-hoc network；dense neural network,；intrusion detection,；deep learning
