An Efficient Dynamic Solution for the Detection and Prevention of Black Hole Attack in VANETs

Abstract

Rapid and tremendous advances in wireless technology, miniaturization, and Internet of things (IoT) technology have brought significant development to vehicular ad hoc networks (VANETs). VANETs and IoT together play a vital role in the current intelligent transport system (ITS). However, a VANET is highly vulnerable to various security attacks due to its highly dynamic, decentralized, open-access medium, and protocol-design-related concerns. Regarding security concerns, a black hole attack (BHA) is one such threat in which the control or data packets are dropped by the malicious vehicle, converting a safe path/link into a compromised one. Dropping data packets has a severe impact on a VANET’s performance and security and may cause road fatalities, accidents, and traffic jams. In this study, a novel solution called detection and prevention of a BHA (DPBHA) is proposed to secure and improve the overall security and performance of the VANETs by detecting BHA at an early stage of the route discovery process. The proposed solution is based on calculating a dynamic threshold value and generating a forged route request (RREQ) packet. The solution is implemented and evaluated in the NS-2 simulator and its performance and efficacy are compared with the benchmark schemes. The results showed that the proposed DPBHA outperformed the benchmark schemes in terms of increasing the packet delivery ratio (PDR) by 3.0%, increasing throughput by 6.15%, reducing the routing overhead by 3.69%, decreasing the end-to-end delay by 6.13%, and achieving a maximum detection rate of 94.66%.

Protecting MANETs from Black and Gray Hole Attacks Through a Detailed Detection System

“…In this section, the SRMAD-AODV protocol is simulated using network simulator (NS2.34). Also, its efficiency is compared to the existing protocols by simulating them for black and gray-hole attacks detection: SRD-AODV [13], ITIM [14], ACIDS [19], ITAODV [20] and DPBHA [21]. This analysis is conducted according to the EED, PDR and throughput.…”

“…Detection and prevention of a black hole attack (DPBHA) [21] were designed to protect and enhance the total confidentiality of the system by recognizing the blackhole attacks at an early phase of the path-finding task. It was depending on the determination of an adaptive threshold and the creation of the fake RREQ packet.…”

A Survey on Blockchain-Based Trust Management for Internet of Things

“…At the second level, the authorized RSUs aggregate the trust scores of each vehicle by a weighted average model and then use PBFT consensus to maintain a consortium blockchain. However, their applied Ad-hoc On-Demand Vector (AODV) protocol suffers from several security vulnerabilities and challenges that cannot be ignored [113]. Trust update is supported in both time-driven and event-driven ways.…”

Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

“…Two layers make up service-oriented VANET's architecture, as shown in [10] and [11]. A dedicated short-range communications (DSRC) standard is used for communications between vehicles and roadside infrastructure (V2I) or between vehicles themselves (V2V) in the first layer, which consists of the wireless communication devices in the RSUs and onboard vehicles [12]. A central authority (CA) and a service provider (SP) server make up the second layer [13].…”