ARPVP: Attack Resilient Position-Based VANET Protocol Using Ant Colony Optimization

Abstract

The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).

Machine learning based novel frameworks developments and architectures for secured communication in VANETs for smart transportation

“…Based on secondary data or alerts that are generated in response to primary alerts for PCN application, researchers [16] have detected misbehavior in VANETs. Concept of a data-centric misbehavior detection method program was utilized in a brand-new misbehavior detection scheme that was presented by [17]. [18] devised a VARM mechanism that gathers information from neighbor transmissions at a single vehicle in order to locate the malicious vehicle.…”

The Evolution of VANET Networks: A Review of Emerging Trends in Artificial Intelligence and Software-Defined Networks

“…This method uses the travel time information measured by the vehicles and is sent through multi-hop communications to calculate the shortest routes. While in [3], the importance of security in the routing layer in IoT networks, including Vanet networks, is discussed. Different solutions for secure routing in IoT networks have been proposed, such as cryptography-based methods and trust-based mechanisms.…”