In recent years, there is rapid development in smart vehicles for transmitting the information, all smart vehicles are
now uses the internet services for communication. So security assurance in vehicular ad-hoc network is a crucial and
challenging task due to open access medium. The main objective of VANET is to improve the safety, comfort, driving efficiency.
This paper may solve the problems existing in intrusion detection in VANET using machine learning, neural network, including
redundant information, large amount of data needed, etc. For proposed intrusion detection system in VANET used Deep Belief
Network (DBN) algorithm of deep learning. Deep Belief Network is an effective method of solving the problems from neural
network with deep layer, such as low velocity and the overfitting phenomenon in learning. . The intrusion detection system for
VANET is used to detect the attack and prevent the network.
