Intrusion Detection for Vehicular Ad Hoc Network Based on Deep Belief Network

There has been continued to be a lot of research into self-driving and semi-self-driving in the last few years, which has to the creation of the vehicular ad hoc networks, but has become more vulnerable to potential attacks due to the misuse of networks. The proposed model of deep learning algorithm, namely deep belief network is used for detecting intrusion in the vehicular ad hoc network (VANET). Deep belief network algorithm gives more accuracy for intrusion detection in the network than existing methodologies such as machine learning algorithms or another deep learning algorithm. Nowadays, automation is more important in all fields, similarly automatic vehicles, i.e., driverless cars. These types of vehicles will come to market and all these vehicles are connected through a wireless network. All the vehicles are communicating with each other by sending some informative packets but there is an attacker who accesses that data and changes the data which may affect the security of the vehicle and also damage the system responsible for the accident. So, intrusion detection system for the vehicular ad hoc network is important with maximum accuracy. For this purpose, we used the updated CICIDS2017 dataset for training, testing and evaluation process. Experimental results using a deep belief network for intrusion detection mechanisms proved that the proposed model could have good results on multiclass and binary classification accuracy 90% and 98% respectively.