基于轻量级密集神经网络的车载自组网入侵检测方法

Intrusion detection method for VANET based on light dense neural network

在车载自组网中,攻击者可以通过伪造、篡改消息等方式发布虚假交通信息,导致交通拥堵甚至是严重的交通事故,而传统的入侵检测方法不能满足车载自组网的应用需求。为了解决现阶段车载网中入侵检测方法性能低且存储与时间成本高的问题,提出了一种基于密集神经网络的入侵检测方法L-DenseNet(Light Dense Neural Network),通过降低模型复杂性,提升算法训练速度和部署适应性,使其更适用于车载自组网中的入侵检测。在VeReMi数据集上进行对比实验,结果表明,该方法在识别各类攻击的精确率和召回率的综合表现最好,且具有较少的时间成本和存储开销。

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.