A probabilistic model to corroborate three attacks in vehicular ad hoc networks

Karim, Laroussi; Boucif, Amar Bensaber; Mesfioui, Mhamed; Biskri, Ismaïl

doi:10.1109/iscc.2015.7405496

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…The proposal is to use " signature-based authentication mechanisms" to reduce the effect of the DOS attack. In the study [42], the authors propose an innovative probabilistic model based on logistic regression. This method makes it possible to estimate the appearance of an attack.…”

Section: Literature Reviewmentioning

confidence: 99%

Soft Labeling and Expectation-Maximization Algorithm to the Learning Problem of Uncertain Labeling in Vehicular Networks

Alaya

Sellami

2023

Preprint

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The supervised and semi-supervised learning framework does not always correspond to the situations encountered in vehicular networks. The labeling work is therefore often laborious and expensive. This is why the development of solutions to deal with imperfect labels was of particular interest to us during this research article. We introduce in this paper the formulation of the classification problem when the information available on the labels of the examples used for learning is imperfect. We also present an efficient way to solve classification problems, even when some of the labels provided to learn the classification function are wrong. Furthermore, we present our work on the extension of statistical learning methods in the environment of vehicular networks. To build an expectation-maximization (EM) algorithm capable of optimizing the marginal log-likelihood of the observed data, the path we will take follows the classic approach encountered in the probabilistic framework. Different experiments were carried out to analyze the behavior of our algorithm when “soft” labels in vehicles are used. These experiences have allowed us in particular to highlight the contribution of "soft" labels in this context to represent information on the reliability of the labels and thus significantly improve the performance of vehicular networks.

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Section: Literature Reviewmentioning

confidence: 99%

Soft Labeling and Expectation-Maximization Algorithm to the Learning Problem of Uncertain Labeling in Vehicular Networks

Alaya

Sellami

2023

Preprint

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“…Logistic regression can be used to find the probability of the event of an attack using Equation (23)

P (Y = 1) = \frac{e^{(NVA 1 x 1 + NVA 2 x 2 + NVA 3 x 3)}}{1 + e^{((), italicNVA 1 x 1 + italicNVA 2 x 2 + italicNVA 3 x 3)}}

where Y is the attack to predict (dependent variable), NV A 1, NV A 2, and NV A 3 are the estimations parameters, X j is the predictor variables (the parameters are the three attacks used in simulation in). Authors in Reference proposed an efficient and lightweight intrusion detection scheme for vehicular network to protect it from the malicious attacks.…”

Section: Countermeasures To Handle Attacks In Vanetmentioning

confidence: 99%

“…where Y is the attack to predict (dependent variable), NV A1, NV A2, and NV A3 are the estimations parameters, X j is the predictor variables (the parameters are the three attacks used in simulation in 69 ). Authors in Reference 70 proposed an efficient and lightweight intrusion detection scheme for vehicular network to protect it from the malicious attacks.…”

Section: Machine Leaning-basedmentioning

confidence: 99%

“…IBV involves a key generation, predistribution, anonymous identity, and batch verification. Sun et al proposed system initialization with authentication, anonymous ID creation, message signing, sending registration requests, sending hello messages, a batch verification, and V2V communication mechanism. It helps in preventing the traceability attacks and maintaining privacy.…”

Section: Countermeasures To Handle Attacks In Vanetmentioning

confidence: 99%

“…Here, the registration overheads can be increased due to reregistration after certain defined time period, and change of road side unit (RSU) under which the vehicle belongs. ID‐based security systems used for privacy, introduces pseudonym‐based scheme for vehicle traceability and privacy. Here, the threshold authentication was implemented using a privacy‐preserving defense scheme, which is helpful to service providers.…”

Section: Countermeasures To Handle Attacks In Vanetmentioning

confidence: 99%

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A systematic review on security issues in vehicular ad hoc network

Tanwar

Vora

Tyagi

et al. 2018

Security and Privacy

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Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation system to provide safety and security alongwith reduction in traffic jam using vehicle‐to‐vehicle (V2V) and vehicle‐to‐roadside (V2R) unit communications. However, one of the major concerns in VANETs is to maintain the privacy and security of the sensitive information shared between vehicles and end‐users, as information leakage can influence any implemented solution in this environment. Keeping view on the above‐mentioned points, this paper provides a detailed survey on various types of attacks and possible solutions to handle these attacks. Using the proposed survey, a detailed taxonomy based upon the types of attacks and possible solutions to handle these attacks is proposed. Finally, various emerging open issues and challenges along with the security threats are identified and discussed in the paper. These open research issues motivate the communities to design new solutions to mitigate the existing new security threats in this area.

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Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

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Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and lowlatency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

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A probabilistic model to corroborate three attacks in vehicular ad hoc networks

Cited by 5 publications

References 11 publications

Soft Labeling and Expectation-Maximization Algorithm to the Learning Problem of Uncertain Labeling in Vehicular Networks

Soft Labeling and Expectation-Maximization Algorithm to the Learning Problem of Uncertain Labeling in Vehicular Networks

A systematic review on security issues in vehicular ad hoc network

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

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