Black hole attacks analysis for AODV and AOMDV routing performance in VANETs

Afdhal, Afdhal; Muchallil, Sayed; Walidainy, Hubbul; Yuhardian, Qodri

doi:10.1109/iceltics.2017.8253244

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…Trust can be demonstrated in various ways. Rejecting all packets from a given node can be one of these ways, as can trusting but not transmitting, and reliable (accepting and transmitting all data) [18][19][20].…”

Section: Figure 2 Example Of Phishing Information Attackmentioning

confidence: 99%

Phishing and Sybil Enhanced Behavior Processing and Footprint Algorithms in Vehicular Ad Hoc Network

Kakulla¹,

Srinivas²

2022

IJSSE

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Communication between vehicles is the core of VANETs, which are built on notions of mobile ad hoc networks (MANETs). Each car on the network is now seen as a mobile network node. All participating cars become wireless routers or nodes, depending on your viewpoint. VANET creates a huge network with a long-range by connecting all cars within range to a fixed unit. VANET assists with traffic regulation, communication between vehicles and the sharing of road data. There is a possibility that the VANET network could be compromised by identity and information concerns, resulting in data delays or theft. Attacks like Sybil and phishing are possible because of this network's weakness. Due to the two recent strikes, infrastructure and human lives may be in jeopardy. There are two novel algorithms developed by the authors to tackle Sybil and Phishing assaults on VANET networks: Phishing and Sybil Enhanced Behavior Processing and Footprints (P&SEBP&F). Originally known as the Phishing and Sybil Enhanced Behavior Processing and Footprint, P&STL&T was renamed P&STL&T. Phishing and Sybil were used as well as varied attacker-to-victim node ratios to test the effectiveness of the new tactics for assessing effectiveness. Compared to previous study and the work of the other authors cited, there were approximately 30% fewer attackers detected during the research.

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Section: Figure 2 Example Of Phishing Information Attackmentioning

confidence: 99%

Phishing and Sybil Enhanced Behavior Processing and Footprint Algorithms in Vehicular Ad Hoc Network

Kakulla¹,

Srinivas²

2022

IJSSE

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“…Then, he/she could exploit existing vulnerabilities in routing protocols, such as Ad hoc On-Demand Distance Vector (AODV) [108], to broadcast bogus routing information to its neighboring nodes. A research analysis conducted by Afdhal et al [109] investigate the impact of the black hole attacks on the performance of AODV and AOMDV (Ad hoc On-demand Multipath Distance Vector) routing protocols in VANET. The goal of the attacker is to convince neighboring nodes that they are on the shortest path in order to increase the likelihood of its route being chosen.…”

Section: B: Black Hole Attacksmentioning

confidence: 99%

A Survey on the Current Security Landscape of Intelligent Transportation Systems

Lamssaggad¹,

Benamar²,

Hafid³

et al. 2021

IEEE Access

112

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With the proliferation of embedded technologies and wireless capabilities, today's vehicles are no longer isolated mechanical machines. They become part of a hyper-connected system-Intelligent Transportation Systems (ITS)-that has the potential to support multiple levels of autonomy and intelligence improving considerably the safety, efficiency, and sustainability of transportation networks. However, this raises new security issues that make the whole system prone to cybersecurity attacks that threaten both the safety and privacy of all road-users. This paper gives a short background tutorial on the main security issues and the different attacks that hinder Intelligent Transport Systems. To enable secure and safe ITS applications, this paper provides a comprehensive analysis of existing solutions and highlights their strengths and limitations. Finally, this survey presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and car manufactures to improve the security of ITS.

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“…They analyzed four protocols which are AODV, OLSR, DSR and DSDV by using NS3 and SUMO. More research done by Ahmed et al [14] analyzed the impact of Blackhole attack on the VANET's reactive and Afdal et al [15] examine and analyze the influence of the black hole attack on the AODV and AOMDV routing protocols performance in vehicular ad hoc networks. They found that both AODV and AOMDV are vulnerable to black hole attacks in VANET.…”

Section: Related Workmentioning

confidence: 99%

Real Time Vehicular Traffic Simulation for Black Hole Attack in the Greater Detroit Area

Alshammari¹,

Zohdy²,

Debnath³

et al. 2020

JIS

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Vehicular Ad-hoc Networks (VANETs) technology has recently emerged, and gaining significant attention from the research because it is promising technologies related to Intelligent Transportation System (ITSs) and smart cities. Wireless vehicular communication is employed to improve traffic safety and to reduce traffic congestion. Each vehicle in the ad-hoc network achieves as a smart mobile node categorized by high mobility and forming of dynamic networks. As a result of the movement of vehicles in a continuous way, VA-NETs are vulnerable to many security threats so it requisites capable and secure communication. Unfortunately, Ad hoc networks are liable to varied attacks like Block Hole attacks and Grey Hole attacks, Denial of service attacks, etc. Among the most known attacks are the Black Hole attacks while the malicious vehicle is able to intercept the data and drops it without forwarding it to the cars. The main goal of our simulation is to analyze the performance impact of black hole attack in real time vehicular traffic in the Greater Detroit Area using NS-2 and SUMO (Simulation of Urban). The simulation will be with AODV protocol.

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Black hole attacks analysis for AODV and AOMDV routing performance in VANETs

Cited by 13 publications

References 9 publications

Phishing and Sybil Enhanced Behavior Processing and Footprint Algorithms in Vehicular Ad Hoc Network

Phishing and Sybil Enhanced Behavior Processing and Footprint Algorithms in Vehicular Ad Hoc Network

A Survey on the Current Security Landscape of Intelligent Transportation Systems

Real Time Vehicular Traffic Simulation for Black Hole Attack in the Greater Detroit Area

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