An Efficient Algorithm for Black Hole Attacks Detection in VANET

Significant progress in wireless technology, smallness, and the Internet of Things (IoT) has led to notable advancements in vehicular ad hoc networks (VANETs). VANETs, in conjunction with Internet of Things, participate a crucial role in today’s intelligent transportation systems. However, VANETs face considerable security risks due to their dynamic nature, decentralized structure, open-access environment, and concerns related to protocol design. Among these security challenges, one of the most significant threats which maliciously disturb the communication of data packets is Black Hole Attack (BHA), thereby reduce the honesty of a previously secure path or link. The act of dropping data packets severely impacts both the performance and security of VANETs, potentially resulting in road accidents, fatalities, and traffic congestion. To address these security concerns, this study proposes a novel solution named An Efficient Algorithm for Black Hole attacks Detection in VANET (EABHD). Proposed algorithms try to improve the safety in vehicular sensor networks by detecting the black hole attack in the starting phase while discovering the route. EABHD is based on the calculation of the threshold value dynamically. This algorithm is evaluated using the Network Simulator 2 and compared against number of algorithms. The output of the proposed algorithms shows the improvement in packet delivery ratio, and throughput and decrease in end-to-end delay and routing overhead by ${3. 1 7 \%}$, ${6. 2 4 \%}$ increase and 6.23% and 3.8%, decrease respectively.