Network data security based on routing algorithm: application in vehicular ad-hoc networks

One crucial element of intelligent transportation systems (ITS) is the use of vehicle ad hoc networks, or VANETs. Among the many advantages, these networks provide are increased road safety and decreased traffic. Despite their many benefits, VANETs are nonetheless vulnerable to a variety of security risks, such as devastating blackhole attacks. The following is a summary of the primary characteristics and contributions this paper made. First, deep learning (DL) capabilities are included into every node in the DLSR protocol, enabling it to build secure routes and decide between secure and conventional routing. Furthermore, we may look at the fitness function value of each choice to decide which is preferable for the next hop by analysing the activity of malicious nodes. Second, it is thought that the DLC protocol acts as a foundation that reduces control overhead and improves node-to-node communication. © 2024 Inderscience Enterprises Ltd.