Intrusion Detection System for SDN based VANETs Using A Deep Belief Network, Decision Tree, and ToN -IoT Dataset

SDN-based VANETs are vehicular ad hoc networks that utilize the software-defined networking (SDN) paradigm for network management and control. The use of SDN in VANETs offers several benefits over traditional VANETs. For example, it allows for a more flexible and dynamic network management, which can respond quickly to changing traffic conditions and network requirements. It also provides a more scalable and efficient mechanism for managing network traffic, which can improve the overall network performance. However, SDN-based VANET has challenges, such as attacks on SDN controllers and injection attacks in the network due to the nature of the SDN network. In this paper, the proposed intrusion detection system (IDS) is based on a deep belief network (DBN) and decision tree (DT) to improve the security level in the network. For the simulation, we used the ToN-IoT dataset to test the proposed work. The proposed work achieves better results than existing works.