Connected Vehicles in the IoV: Concepts, Technologies and Architectures

Mahmood, Zaigham

doi:10.1007/978-3-030-36167-9_1

Cited by 49 publications

(32 citation statements)

References 2 publications

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“…The possibility of vehicles having multiple pseudonyms available for use at a given time introduces the risk of potential Sybil attacks [11]. This is when an adversary claims to be multiple vehicles with different identities and acts maliciously to mislead other vehicles to make harmful decisions [16]. Selfissuance approaches are known to be more difficult to avoid Sybil attacks because of the higher level of autonomy of vehicles [2].…”

Section: ) Pseudonym Lifecyclementioning

confidence: 99%

A Blockchain Assisted Vehicular Pseudonym Issuance and Management System for Conditional Privacy Enhancement

Chulerttiyawong

Jamalipour

2021

IEEE Access

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A feasible approach commonly discussed in the literature for mitigating location privacy threats in vehicular ad hoc networks (VANETs) is the use of pseudonyms instead of real vehicle identifications. However, for relevant authorities to be able to identify misbehaving vehicles through their pseudonyms, it is essential that the privacy protection mechanisms only allow for conditional anonymity and not complete anonymity. In this paper, we propose the use of a permissioned consortium blockchain system with smart contract feature to facilitate secure and conditional privacy-preserving vehicular pseudonym issuance and management in a multi-jurisdictional road network. The use of a permissioned consortium blockchain helps mitigate security risks associated with the complexities in interorganizational data handling, such as in the areas of access control, data integrity, confidentiality, and availability. The proposed system architecture takes advantage of the predicted wide availability of Roadside Units (RSUs), and the highly viable, flexible and mature Public Key Infrastructure (PKI) technology for usage in vehicular pseudonymous communications. We successfully carried out a small-scale simulation of the proposed architecture using the Vehicles in Network Simulation (Veins) platform for integrated traffic and network simulation services (SUMO as the traffic simulator and OMNeT++ as the network simulator), and the Hyperledger Fabric platform as the permissioned consortium blockchain system. Simulation and performance analysis results reveal the feasibility of practical deployment of the scheme, and show that the scheme addresses the identified shortfalls of existing works, including the ability to achieve a better balance between connectivity and storage requirements.

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Section: ) Pseudonym Lifecyclementioning

confidence: 99%

A Blockchain Assisted Vehicular Pseudonym Issuance and Management System for Conditional Privacy Enhancement

Chulerttiyawong

Jamalipour

2021

IEEE Access

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“…Other technologies are also used like sensor technologies [39]. These technologies are all being proposed to find the best solutions that are needed for a secure, reliable and autonomous vehicular networks.…”

Section: Iov Overviewmentioning

confidence: 99%

Towards a secure ITS: Overview, challenges and solutions

Sleem

Noura

Couturier

2020

Journal of Information Security and Applications

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Intelligent Transportation Systems (ITS), the heart of the new revolution of smart transport, has evolved from the well-known Vehicular Ad-hoc Networks (VANETs) to become the Internet of Vehicles (IoV). In fact, the increase in the number of vehicles and the newly born technologies have stimulated the new Internet of Vehicles (IoV) or the Internet of Cars. In general, ITS aims at ensuring better traffic efficiency and reducing road accidents. However, due to different limitations and issues, these systems suffer from different security and privacy vulnerabilities. In fact, they are both vulnerable to various types of security and privacy attacks that may result in life-endangering situations. As a result, several solutions were presented to achieve the required levels of security and confidentiality. In this paper, an overview of ITS is presented stating the reasons behind the evolution from Vanet to IoV. Then, the main threats/attacks that threaten ITS are classified according to their (1) security impact and according to the (2) network layer(s) they affect. Solutions for each attack are also well presented. In addition, a security and performance evaluation and summary tables are presented to provide an overview of these surveyed solutions.

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“…HE far-ranging impact of the Internet of Things (IoTs) is expected to grow manifold where connected devices would increase up to 64 billion by 2025 from 10 billion in 2018 [1]. This mammoth increase in connected devices would be courtesy to connected artifacts over transportation media including Maritime Transportation Systems (MTS) as autonomous units.…”

Section: Introductionmentioning

confidence: 99%

Securing IoT Based Maritime Transportation System Through Entropy-Based Dual-Stack Machine Learning Framework

Ali

Sarwar

Shafi

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Internet of Things (IoTs) is envisaged to widely capture the realm of logistics and transportation services in future. The applications of ubiquitous IoTs have been extended to Maritime Transportation Systems (MTS) that spawned increasing security threats; posing serious fiscal concerns to stakeholders involved. Among these threats, Distributed Denial of Service Attack (DDoS) is ranked very high and can wreak havoc on IoT artifacts of MTS network. Timely and effective detection of such attacks is imperative for necessary mitigation. Conventional approaches exploit entropy of attributes in network traffic for detecting DDoS attacks. However, majority of these approaches are static in nature and evaluate only a few network traffic parameters, limiting the number of DDoS attack detection to a few types and intensities. In current research, a novel framework named "Dual Stack Machine Learning (S2ML)" has been proposed to calculate distinct entropy-based varying 10-Tuple (T) features from network traffic features, three window sizes and associated Rate of Exponent Separation (RES). These features have been exploited for developing an intelligent model over MTS-IoT datasets to successfully detect multiple types of DDoS attacks in MTS. S2ML is an efficient framework that overcomes the shortcomings of prevalent DDoS detection approaches, as evident from the comparison with Multi-layer Perceptron (MLP), Alternating Decision Tree (ADT) and Simple Logistic Regression (SLR) over different evaluation metrics (Confusion metrics, ROCs). The proposed S2ML technique outperforms prevalent ones with 1.5% better results compared to asserted approaches on distribution of normal/attack traffic. We look forward to enhance the model performance through dynamic windowing, measuring packet drop rates and infrastructure of Software Defined Networks (SDNs).

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Connected Vehicles in the IoV: Concepts, Technologies and Architectures

Cited by 49 publications

References 2 publications

A Blockchain Assisted Vehicular Pseudonym Issuance and Management System for Conditional Privacy Enhancement

A Blockchain Assisted Vehicular Pseudonym Issuance and Management System for Conditional Privacy Enhancement

Towards a secure ITS: Overview, challenges and solutions

Securing IoT Based Maritime Transportation System Through Entropy-Based Dual-Stack Machine Learning Framework

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