Biblio
Significant developments have taken place over the past few years in the area of vehicular communication systems in the ITS environment. It is vital that, in these environments, security is considered in design and implementation since compromised vulnerabilities in one vehicle can be propagated to other vehicles, especially given that V2X communication is through an ad-hoc type network. Recently, many standardisation organisations have been working on creating international standards related to vehicular communication security and the so-called Internet of Vehicles (IoV). This paper presents a discussion of current V2X communications cyber security issues and standardisation approaches being considered by standardisation bodies such as the ISO, the ITU, the IEEE, and the ETSI.
Modern vehicles in Intelligent Transportation Systems (ITS) can communicate with each other as well as roadside infrastructure units (RSUs) in order to increase transportation efficiency and road safety. For example, there are techniques to alert drivers in advance about traffic incidents and to help them avoid congestion. Threats to these systems, on the other hand, can limit the benefits of these technologies. Securing ITS itself is an important concern in ITS design and implementation. In this paper, we provide a security model of ITS which extends the classic layered network security model with transportation security and information security, and gives a reference for designing ITS architectures. Based on this security model, we also present a classification of ITS threats for defense. Finally a proof-of-concept example with malicious nodes in an ITS system is also given to demonstrate the impact of attacks. We analyzed the threat of malicious nodes and their effects to commuters, like increasing toll fees, travel distances, and travel times etc. Experimental results from simulations based on Veins shows the threats will bring about 43.40% more total toll fees, 39.45% longer travel distances, and 63.10% more travel times.
Cooperative Intelligent Transport Systems (C-ITS) are expected to play an important role in our lives. They will improve the traffic safety and bring about a revolution on the driving experience. However, these benefits are counterbalanced by possible attacks that threaten not only the vehicle's security, but also passengers' lives. One of the most common attacks is the Sybil attack, which is even more dangerous than others because it could be the starting point of many other attacks in C-ITS. This paper proposes a distributed approach allowing the detection of Sybil attacks by using the traffic flow theory. The key idea here is that each vehicle will monitor its neighbourhood in order to detect an eventual Sybil attack. This is achieved by a comparison between the real accurate speed of the vehicle and the one estimated using the V2V communications with vehicles in the vicinity. The estimated speed is derived by using the traffic flow fundamental diagram of the road's portion where the vehicles are moving. This detection algorithm is validated through some extensive simulations conducted using the well-known NS3 network simulator with SUMO traffic simulator.
Vehicular Ad-hoc Networks (VANETs) are a subset of Mobile Ad-hoc Networks (MANETs). They are deployed to introduce the ability of inter-communication among vehicles in order to guarantee safety and provide services for people while driving. VANETs are exposed to many types of attacks like denial of service, spoofing, ID disclosure and Sybil attacks. In this paper, a novel lightweight approach for preventing Sybil attack in VANETs is proposed. The presented protocol scheme uses symmetric key encryption and authentication between Road Side Units (RSUs) and vehicles on the road so that no malicious vehicle could gain more than one identity inside the network. This protocol does not need managers for Road Side Units (RSUs) or Certification Authority (CA) and uses minimum amount of messages exchanged with RSU making the scheme efficient and effective.
Communicating vehicles will change road traffic as we know it. With current versions of European and US standards in mind, the authors discuss privacy and traffic surveillance issues in vehicular network technology and outline research directions that could address these issues.
LTE-based Device-to-Device (D2D) communications have been envisioned as a new key feature for short range wireless communications in advanced and beyond 4G networks. We propose in this work to exploit this novel concept of D2D as a new alternative for Intelligent Transportation Systems (ITS) Vehicle-to-Vehicle/Infrastructure (V2X) communications in next generation cellular networks. A 3GPP standard architecture has been recently defined to support Proximity Services (ProSe) in the LTE core network. Taking into account the limitations of this latter and the requirements of ITS services and V2X communications, we propose the CVN solution as an enhancement to the ProSe architecture in order to support hyper-local ITS services. CVN provides a reliable and scalable LTE-assisted opportunistic model for V2X communications through a distributed ProSe architecture. Using a hybrid clustering approach, vehicles are organized into dynamic clusters that are formed and managed by ProSe Cluster Heads which are elected centrally by the CVN core network. ITS services are deemed as Proximity Services and benefit from the basic ProSe discovery, authorization and authentication mechanisms. The CVN solution enhances V2V communication delays and overhead by reducing the need for multi-hop geo-routing. Preliminary simulation results show that the CVN solution provides short setup times and improves ITS communication delays.
The technology of vehicle video detecting and tracking has been playing an important role in the ITS (Intelligent Transportation Systems) field during recent years. The occlusion phenomenon among vehicles is one of the most difficult problems related to vehicle tracking. In order to handle occlusion, this paper proposes an effective solution that applied Markov Random Field (MRF) to the traffic images. The contour of the vehicle is firstly detected by using background subtraction, then numbers of blocks with vehicle's texture and motion information are filled inside each vehicle. We extract several kinds of information of each block to process the following tracking. As for each occlusive block two groups of clique functions in MRF model are defined, which represents spatial correlation and motion coherence respectively. By calculating each occlusive block's total energy function, we finally solve the attribution problem of occlusive blocks. The experimental results show that our method can handle occlusion problems effectively and track each vehicle continuously.