A secure lightweight authentication model with interference aware routing and attack detection approach in VANET

A secure lightweight authentication model with interference aware routing and attack detection approach in VANET

연구 분야: Cryptography

학회: Cluster Computing

In Vehicular Ad Hoc network (VANET), security is one of the main concerns that are required to be analysed seriously. In VANET, the nodes are movable and they communicate with one another and the Roadside Unit (RSU). Moreover, the network in VANET is self-formed, and unpredictable so that each node in VANET can communicate with one another on demand, which consequences in a security breach when there is an attack originated into the network. Generally, various kinds of attacks exist in VANET in which the RSU and vehicles are susceptible to the attackers. In this research, a secure authentication model with routing and attack mitigation is carried out using the devised Secureauth protocol and optimal deep learning algorithm. Here, the security of the devised SecureAuth protocol is enhanced based on the security parameters, such as interpolation, hashing function and EX-OR operation. Moreover, the Cluster head (CH) selection and routing are carried out using fuzzy logic and the Fractional Aquila remora optimizer algorithm (Fr-ARO) algorithm. In addition, the attack detection is done by the Deep Maxout Network (DMN) in which the weights and bias of DMN are trained with the Fractional Aquila Spider Monkey Optimization algorithm (FASMO) algorithm. Furthermore, the secureauth protocol is analysed with other present techniques based on the energy, trust, precision, recall, computation time, and memory usage values of 0.955 J, 0.959, 0.947, 0.964, 200.878 ms, and 71.977 Mb.