A Survey on Security in VANETs

T, Pavithra; Nagabhushana, B. S.

doi:10.1109/icirca48905.2020.9182823

Cited by 18 publications

(12 citation statements)

References 37 publications

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“…Table 1, shows the probability of dishonesty of vehicles. For each vector ⃗ X id = (x 1 , x 2 , x 3 ) in a period T, a probability of dishonesty corresponding to this vector is calculated via the Bayesian filter, which will be detailed in this section based on Equation (5). Then if the probability of dishonesty in the time interval t is above a fixed threshold, this value is equal to 1; otherwise, it is equal to 0.…”

Section: • Hsmentioning

confidence: 99%

“…4 However, these networks are always vulnerable F I G U R E 1 System model of vehicular network and prone to attacks due to the nature of wireless communication. We can classify attacks into six groups based on the protocol stack as follows: 5 1. Network layer (eg, Black hole attack, cooperative black hole attack, Sybil attack, Byzantine, and location disclosure attack).…”

Section: Introductionmentioning

confidence: 99%

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Decentralized prediction and reputation approach in vehicular networks

Najafi

Khoukhi

Lemercier

2022

Trans Emerging Tel Tech

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A vehicle ad-hoc network (VANET) is an essential component of the intelligent transportation system. In VANET, vehicle-to-vehicle communication plays a significant role in providing a secure network and safe exchange of information between nodes. However, VANET is prone to critical risks, threats, and attacks due to its unique characteristics and dynamic topology. Trust is one of the most critical aspects of security. So, this concept has already proved its accuracy and efficiency in various wireless applications. To ensure trust in vehicular communications, we propose a decentralized prediction and reputation approach that includes two models. First, we introduce a reputation model based on the Bayesian theorem and watchdog concept. We investigate the vehicle's behavior in the network to measure the vehicle's trust rate. Then, we present a prediction model to anticipate the vehicle's state and detect malicious vehicles. Based on this approach, vehicles can avoid interacting with unreliable vehicles and ignore incoming messages from them to reduce the effect of malicious nodes in the network. Our extensive simulations show the performance and accuracy of our approach. Also, the certainty rate remains between 91% and 99% regardless of the simulation scenario's duration. This simulation also demonstrates our classification's certainty, even under different network situations.

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Section: • Hsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized prediction and reputation approach in vehicular networks

Najafi

Khoukhi

Lemercier

2022

Trans Emerging Tel Tech

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“…Security and privacy issues posed by the underlying IP protocol stack in VANET [181][182][183] are naturally resolved by the core characteristics of NDN. However, new security and privacy challenges, specific for the underlying NDN architecture 54 still need to be tackled.…”

Section: Research Question (Rq3)-security Privacy and Trustmentioning

confidence: 99%

On the realization of VANET using named data networking: On improvement of VANET using NDN‐based routing, caching, and security

Silva

Costa

Macedo

2022

Int J Communication

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Summary Named data networking (NDN) presents a huge opportunity to tackle some of the unsolved issues of IP‐based vehicular ad hoc networks (VANET). The core characteristics of NDN such as the name‐based routing, in‐network caching, and built‐in data security provide better management of VANET proprieties (e.g., the high mobility, link intermittency, and dynamic topology). This study aims at providing a clear view of the state‐of‐the‐art on the developments in place, in order to leverage the characteristics of NDN in VANET. We resort to a systematic literature review (SLR) to perform a reproducible study, gathering the proposed solutions and summarizing the main open challenges on implementing NDN‐based VANET. There exist several related studies, but they are more focused on other topics such as forwarding. This work specifically restricts the focus on VANET improvements by NDN‐based routing (not forwarding), caching, and security. The surveyed solution herein presented is performed between 2010 and 2021. The results show that proposals on the selected topics for NDN‐based VANET are recent (mainly from 2016 to 2021). Among them, caching is the most investigated topic. Finally, the main findings and the possible roadmaps for further development are highlighted.

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“…1. The data collection process begins by taking data from various incorporated sensors and then performing aggregation and compression to reduce communication cost and time, depending upon the protocol and adapted scheme [9]. Data collection protocols are designed by considering both predictable movement patterns and abrupt variation in speed.…”

Section: Introductionmentioning

confidence: 99%

Data collection protocols for VANETs: a survey

Gillani

Niaz

Farooq

et al. 2022

Complex Intell. Syst.

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We live in the era of Intelligent Transport Systems (ITS), which is an extension of Vehicular AdHoc Networks (VANETs). In VANETs, vehicles act as nodes connected with each other and sometimes with a public station. Vehicles continuously exchange and collect information to provide innovative transportation services; for example, traffic management, navigation, autonomous driving, and the generation of alerts. However, VANETs are extremely challenging for data collection, due to their high mobility and dynamic network topologies that cause frequent link disruptions and make path discovery difficult. In this survey, various state-of-the-art data collection protocols for VANETs are discussed, based on three broad categories, i.e., delay-tolerant, best-effort, and real-time protocols. A taxonomy is designed for data collection protocols for VANETs that is essential to add precision and ease of understandability. A detailed comparative analysis among various data collection protocols is provided to highlight their functionalities and features. Protocols are evaluated based on three parametric phases. First, protocols investigation based on six necessary parameters, including delivery and drop ratio, efficiency, and recovery strategy. Second, a 4-D functional framework is designed to fit most data collection protocols for quick classification and mobility model identification, thus eradicating the need to read extensive literature. In the last, in-depth categorical mapping is performed to deep dive for better and targeted interpretation. In addition, some open research challenges for ITS and VANETs are discussed to highlight research gaps. Our work can thus be employed as a quick guide for researchers to identify the technical relevance of data collection protocols of VANETs.

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A Survey on Security in VANETs

Cited by 18 publications

References 37 publications

Decentralized prediction and reputation approach in vehicular networks

Decentralized prediction and reputation approach in vehicular networks

On the realization of VANET using named data networking: On improvement of VANET using NDN‐based routing, caching, and security

Data collection protocols for VANETs: a survey

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