A Taxonomy of Security Attacks and Issues in Vehicular Ad-Hoc Networks (VANETs)

Tyagi, Parul; Dembla, Deepak

doi:10.5120/15893-5040

Cited by 12 publications

(5 citation statements)

References 16 publications

(15 reference statements)

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“…b) Data storage areas: Drones often capture highresolution images and videos, storing them for later retrieval.The primary data storage area in most drones is the onboard memory or removable storage, often in the form of SD or microSD cards [77]. Beyond mere storage, these areas often come equipped with data management systems.…”

Section: A Physical Attacksmentioning

confidence: 99%

Understanding and Securing Unmanned Aerial Vehicle (UAV) Services: A Comprehensive Tutorial

Anagnostis,

Kotzanikolaou,

Douligeris

2024

Preprint

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Section: A Physical Attacksmentioning

confidence: 99%

Understanding and Securing Unmanned Aerial Vehicle (UAV) Services: A Comprehensive Tutorial

Anagnostis,

Kotzanikolaou,

Douligeris

2024

Preprint

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“…VANET communications can be vulnerable to various types of attacks [7], [11] from different types of attackers. Based on the nature of the attack and motivation of the attacker, they are typically classified as [12]:…”

Section: Overview Of Vanet Misbehavior Detectionmentioning

confidence: 99%

Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach

Sharma

Jaekel

2022

IEEE Open J. Veh. Technol.

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Vehicular ad-hoc network (VANET) is an emerging technology for vehicle-to-vehicle communication vital for reducing road accidents and traffic congestion in an Intelligent Transportation System (ITS). VANET communication is vulnerable to various attacks and cryptographic techniques are commonly used for message integrity and authentication of vehicles. However, cryptograhpic techniques alone may not be sufficient to protect against insider attacks. Many VANET safety applications rely on periodic broadcast of basic safety messages (BSMs) from surrounding vehicles that contain important status information about a vehicle such as its position, speed, and heading. If an attacker (misbehaving vehicle) injects false position information in a BSM, it can lead to serious consequences including traffic congestion or even accidents. Therefore, it is imperative to accurately detect and identify such attackers to ensure safety in the network. This paper presents a novel data-centric approach to detect position falsification attacks, using machine learning (ML) algorithms. Unlike existing techniques, the proposed approach combines information from 2 consecutive BSMs for training and testing. Simulations using the Vehicular Reference Misbehavior (VeReMi) dataset demonstrate that the proposed model clearly outperforms existing approaches for identifying a range of different attack types.

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“…A novel solution has been designed requiring a new link to undergo a vetting period during which its latency is evaluated. In [11], the authors explored the impact of different PAI species, fabricated with different materials, on several local-based descriptors combined with the Fisher Vector feature encoding, in order to increase the robustness to unknown attacks. In [12], a different Message Fabrication Detection mechanism, called MEFAD scheme were proposed.…”

Section: Related Workmentioning

confidence: 99%

Source Fabrication Detection Model based on Key-value Variables in Reactive Protocols of VANET

Chbib

Fahs

Haydar

et al. 2020

International Journal of Circuits, Systems and Signal Processing

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The vehicular communication hasbeen considered as the most promising wirelesscommunication technology in the computernetwork scenario, the beginning of which hasmarked a great change for the passengers in therange of safety application. The development ofvehicular communication increased securitythreats and weaknesses. Vehicularcommunication is exposed to severalvulnerabilities such as Denial of Service attacks(DoS), Black hole and fabrication attacks.Fabrication attack consists of a malicious nodethat modifies information in the packet causingcritical damage in the network like congestionand high delay. In this paper, we propose a novelfabrication model which consists of twoalgorithms one for source attack and another foranti-source attack. In such attack, the maliciousnode fabricates the source address of the routerequest message; this means that the maliciousnode selects randomly from the routing table asource address that is different from the inputsource address and the source address of thecurrent node and forwards the message to itsneighbors. In the anti-attack, our novel proposedalgorithm has the role to identify the sourceattack during the communication. We create twovariables at each node in order to check if theinput source address in RREQ is equal to theoutput source address in RREQ; otherwise, thenode is identified as a malicious node and anurgent message is broadcasted to all nodes toremove it from their routing table. The proposedalgorithm targets to minimize the delay ofpackets. Our simulation is done using SUMO0.22 simulator, NS-2.35 and awk scripts; thesimulation was applied on Hamra area (Beirut,Lebanon). The results show good improvementsin terms of packet delivery ratio and the end toend delay.

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A Taxonomy of Security Attacks and Issues in Vehicular Ad-Hoc Networks (VANETs)

Cited by 12 publications

References 16 publications

Understanding and Securing Unmanned Aerial Vehicle (UAV) Services: A Comprehensive Tutorial

Understanding and Securing Unmanned Aerial Vehicle (UAV) Services: A Comprehensive Tutorial

Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach

Source Fabrication Detection Model based on Key-value Variables in Reactive Protocols of VANET

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