Biblio

Problems such as the increase in the number of private vehicles with the population, the rise in environmental pollution, the emergence of unmet infrastructure and resource problems, and the decrease in time efficiency in cities have put local governments, cities, and countries in search of solutions. These problems faced by cities and countries are tried to be solved in the concept of smart cities and intelligent transportation by using information and communication technologies in line with the needs. While designing intelligent transportation systems (ITS), beyond traditional methods, big data should be designed in a state-of-the-art and appropriate way with the help of methods such as artificial intelligence, machine learning, and deep learning. In this study, a data-driven decision support system model was established to help the business make strategic decisions with the help of intelligent transportation data and to contribute to the elimination of public transportation problems in the city. Our study model has been established using big data technologies and business intelligence technologies: a decision support system including data sources layer, data ingestion/ collection layer, data storage and processing layer, data analytics layer, application/presentation layer, developer layer, and data management/ data security layer stages. In our study, the decision support system was modeled using ITS data supported by big data technologies, where the traditional structure could not find a solution. This paper aims to create a basis for future studies looking for solutions to the problems of integration, storage, processing, and analysis of big data and to add value to the literature that is missing within the framework of the model. We provide both the lack of literature, eliminate the lack of models before the application process of existing data sets to the business intelligence architecture and a model study before the application to be carried out by the authors.

The proliferation of autonomous and connected vehicles on our roads is increasingly felt. However, the problems related to the optimization of the energy consumed, to the safety, and to the security of these do not cease to arise on the tables of debates bringing together the various stakeholders. By focusing on the security aspect of such systems, we can realize that there is a family of problems that must be investigated as soon as possible. In particular, those that may manifest as the system expands. Therefore, this work aims to model and simulate the behavior of a system of autonomous and connected vehicles in the face of a malware invasion. In order to achieve the set objective, we propose a model to our system which is inspired by those used in epidimology, such as SI, SIR, SIER, etc. This being adapted to our case study, stochastic processes are defined in order to characterize its dynamics. After having fixed the values of the various parameters, as well as those of the initial conditions, we run 100 simulations of our system. After which we visualize the results got, we analyze them, and we give some interpretations. We end by outlining the lessons and recommendations drawn from the results.

The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach's effectiveness.

Vehicular Ad-Hoc Network (VANET) is a subcategory of Intelligent Transportation Systems (ITS) that allows vehicles to communicate with other vehicles and static roadside infrastructure. However, the integration of cyber and physical systems introduce many possible points of attack that make VANET vulnerable to cyber attacks. In this paper, we implemented a machine learning-based intrusion detection system that identifies False Data Injection (FDI) attacks on a vehicular network. A co-simulation framework between MATLAB and NS-3 is used to simulate the system. The intrusion detection system is installed in every vehicle and processes the information obtained from the packets sent by other vehicles. The packet is classified into either trusted or malicious using Support Vector Machines (SVM). The comparison of the performance of the system is evaluated in different scenarios using the following metrics: classification rate, attack detection rate, false positive rate, and detection speed. Simulation results show that the SVM-based IDS is able to provide high accuracy detection, low false positive rate, consequently improving the traffic congestion in the simulated highway.

The fields of transportation and fleet management have been evolving at a rapid pace and most of these changes are due to numerous incremental developments in the area. However, a comprehensive study that critically compares and contrasts all the existing techniques and methodologies in the area is still missing. This paper presents a comparative analysis of the vulnerabilities and security threats for IoT and their mitigation strategies in the context of transportation and fleet management. Moreover, we attempt to classify the existing strategies based on their underlying principles.

The maturity of intelligent transportation system, cloud computing and Internet of Things (IoT) technology has encouraged the rapid growth of vehicular ad-hoc networks (VANETs). Currently, vehicles are supposed to carry relatively more storage, on board computing facilities, increased sensing power and communication systems. In order to cope with real world demands such as low latency, low storage cost, mobility, etc., for the deployment of VANETs, numerous attempts have been taken to integrate fog-computing with VANETs. In the recent past, Ma et al. (IEEE Internet of Things, pp 2327-4662, 10. 1109/JIOT.2019.2902840) designed “An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks”. Ma et al. claimed that their protocol offers secure communication in fog-based VANETs and is resilient against several security attacks. However, this comment demonstrates that their scheme is defenseless against vehicle-user impersonation attack and reveals secret keys of vehicle-user and fog-node. Moreover, it fails to offer vehicle-user anonymity and has inefficient login phase. This paper also gives some essential suggestions on strengthening resilience of the scheme, which are overlooked by Ma et al.

Intelligent Transportation Systems (ITS) play a vital role in the development of smart cities. They enable various road safety and efficiency applications such as optimized traffic management, collision avoidance, and pollution control through the collection and evaluation of traffic data from Road Side Units (RSUs) and connected vehicles in real time. However, these systems are highly vulnerable to data corruption attacks which can seriously influence their decision-making abilities. Traditional attack detection schemes do not account for attackers' sophisticated and evolving strategies and ignore the ITS's constraints on security resources. In this paper, we devise a security game model that allows the defense mechanism deployed in the ITS to optimize the distribution of available resources for attack detection while considering mixed attack strategies, according to which the attacker targets multiple RSUs in a distributed fashion. In our security game, the utility of the ITS is quantified in terms of detection rate, attack damage, and the relevance of the information transmitted by the RSUs. The proposed approach will enable the ITS to mitigate the impact of attacks and increase its resiliency. The results show that our approach reduces the attack impact by at least 20% compared to the one that fairly allocates security resources to RSUs indifferently to attackers' strategies.

Improved safety, high mobility and environmental concerns in transportation systems across the world and the corresponding developments in information and communication technologies continue to drive attention towards Intelligent Transportation Systems (ITS). This is evident in advanced driver-assistance systems such as lane departure warning, adaptive cruise control and collision avoidance. However, in connected and autonomous vehicles, the efficient functionality of these applications depends largely on the ability of a vehicle to accurately predict it operating parameters such as location and speed. The ability to predict the immediate future/next location (or speed) of a vehicle or its ability to predict neighbors help in guaranteeing integrity, availability and accountability, thus boosting safety and resiliency of the Vehicular Network for Mobile Cyber Physical Systems (VCPS). In this paper, we proposed a secure movement-prediction for connected vehicles by using Kalman filter. Specifically, Kalman filter predicts the locations and speeds of individual vehicles with reference to already observed and known information such posted legal speed limit, geographic/road location, direction etc. The aim is to achieve resilience through the predicted and exchanged information between connected moving vehicles in an adaptive manner. By being able to predict their future locations, the following vehicle is able to adjust its position more accurately to avoid collision and to ensure optimal information exchange among vehicles.

Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream videos. One interesting application is the Intelligent Transportation Systems (ITS) and public safety applications where drones record videos and send them to a control center for further analysis. These videos are shared by various clients such as law enforcement or emergency personnel. In such cases, the recording might include faces of civilians or other sensitive information that might pose privacy concerns. While the video can be encrypted and stored in the cloud that way, it can still be accessed once the keys are exposed to third parties which is completely insecure. To prevent such insecurity, in this paper, we propose proxy re-encryption based sharing scheme to enable third parties to access only limited videos without having the original encryption key. The costly pairing operations in proxy re-encryption are not used to allow rapid access and delivery of the surveillance videos to third parties. The key management is handled by a trusted control center, which acts as the proxy to re-encrypt the data. We implemented and tested the approach in a realistic simulation environment using different resolutions under ns-3. The implementation results and comparisons indicate that there is an acceptable overhead while it can still preserve the privacy of drivers and passengers.

With self-driving cars making their way on to our roads, we ask not what it would take for them to gain acceptance among consumers, but what impact they may have on other drivers. How they will be perceived and whether they will be trusted will likely have a major effect on traffic flow and vehicular safety. This work first undertakes an exploratory factor analysis to validate a trust scale for human-robot interaction and shows how previously validated metrics and general trust theory support a more complete model of trust that has increased applicability in the driving domain. We experimentally test this expanded model in the context of human-automation interaction during simulated driving, revealing how using these dimensions uncovers significant biases within human-robot trust that may have particularly deleterious effects when it comes to sharing our future roads with automated vehicles.

The idea behind collective perception is to improve vehicles' awareness about their surroundings. Every vehicle shares information describing its perceived environment by means of V2X communication. Similar to other information shared using V2X communication, collective perception information is potentially safety relevant, which means there is a need to assess the reliability and quality of received information before further processing. Transmitted information may have been forged by attackers or contain inconsistencies e.g. caused by malfunctions. This paper introduces a novel approach for estimating a belief that a pair of entities, e.g. two remote vehicles or the host vehicle and a remote vehicle, within a Vehicular ad hoc Network (VANET) are both trustworthy. The method updates the belief based on the consistency of the data that both entities provide. The evaluation shows that the proposed method is able to identify forged information.

With the development of IoT and 5G networks, the demand for the next-generation intelligent transportation system has been growing at a rapid pace. Dynamic mapping has been considered one of the key technologies to reduce traffic accidents and congestion in the intelligent transportation system. However, as the number of vehicles keeps growing, a huge volume of mapping traffic may overload the central cloud, leading to serious performance degradation. In this paper, we propose and prototype a CUPS (control and user plane separation)-based edge computing architecture for the dynamic mapping and quantify its benefits by prototyping. There are a couple of merits of our proposal: (i) we can mitigate the overhead of the networks and central cloud because we only need to abstract and send global dynamic mapping information from the edge servers to the central cloud; (ii) we can reduce the response latency since the dynamic mapping traffic can be isolated from other data traffic by being generated and distributed from a local edge server that is deployed closer to the vehicles than the central server in cloud. The capabilities of our system have been quantified. The experimental results have shown our system achieves throughput improvement by more than four times, and response latency reduction by 67.8% compared to the conventional central cloud-based approach. Although these results are still obtained from the preliminary evaluations using our prototype system, we believe that our proposed architecture gives insight into how we utilize CUPS and edge computing to enable efficient dynamic mapping applications.

The paper considers data security issues in vehicle-to-infrastructure communications, where vehicles stream data to a road side unit. We assume aggregated data in road side units can be stored or used for data analytics. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicle layer, where a group leader is assigned to communicate with group devices and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality of sensory data.

Many technological cases exploiting data science have been realized in recent years; machine learning, Internet of Things, and stream data processing are examples of this trend. Other advanced applications have focused on capturing the value from streaming data of different objects of transport and traffic management in an Intelligent Transportation System (ITS). In this context, security control and trust level play a decisive role in the sustainable adoption of this trend. However, conceptual work integrating the security approaches of different disciplines into one coherent reference architecture is limited. The contribution of this paper is a reference architecture for ITS security (called SITS). In addition, a classification of Big Data technologies, products, and services to address the ITS trust challenges is presented. We also proposed a novel multi-tier ITS security framework for validating the usability of SITS with business intelligence development in the enterprise domain.

In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.

The use of Electric Vehicle (EV) is growing rapidly due to its environmental benefits. However, the major problem of these vehicles is their limited battery, the lack of charging stations and the re-charge time. Introducing Information and Communication Technologies, in the field of EV, will improve energy efficiency, energy consumption predictions, availability of charging stations, etc. The Internet of Vehicles based only on Electric Vehicles (IoEV) is a complex system. It is composed of vehicles, humans, sensors, road infrastructure and charging stations. All these entities communicate using several communication technologies (ZigBee, 802.11p, cellular networks, etc). IoEV is therefore vulnerable to significant attacks such as DoS, false data injection, modification. Hence, security is a crucial factor for the development and the wide deployment of Internet of Electric Vehicles (IoEV). In this paper, we present an overview of security issues of the IoEV architecture and we highlight open issues that make the IoEV security a challenging research area in the future.

Internet of Vehicle (IoV) is an essential part of the Intelligent Transportation system (ITS) which is growing exponentially in the automotive industry domain. The term IoV is used in this paper for Internet of Vehicles. IoV is conceptualized for sharing traffic, safety and several other vehicle-related information between vehicles and end user. In recent years, the number of connected vehicles has increased allover the world. Having information sharing and connectivity as its advantage, IoV also faces the challenging task in the cybersecurity-related matters. The future consists of crowded places in an interconnected world through wearable's, sensors, smart phones etc. We are converging towards IoV technology and interactions with crowded space of connected peoples. However, this convergence demands high-security mechanism from the connected crowd as-well-as other connected vehicles to safeguard of proposed IoV system. In this paper, we coin the term of smart people crowd (SPC) and the smart vehicular crowd (SVC) for the Internet of Vehicles (IoV). These specific crowds of SPC and SVC are the potential cyber attackers of the smart IoV. People connected to the internet in the crowded place are known as a smart crowd. They have interfacing devices with sensors and the environment. A smart crowd would also consist of the random number of smart vehicles. With the future converging in to the smart connected framework for crowds, vehicles and connected vehicles, we present a novel cyber-physical surveillance system (CPSS) framework to tackle the security threats in the crowded environment for the smart automotive industry and provide the cyber security mechanism in the crowded places. We also describe an overview of use cases and their security challenges on the Internet of Vehicles.

Vehicular ad hoc network is one of most recent research areas to deploy intelligent Transport System. Due to their highly dynamic topology, energy constrained and no central point coordination, routing with minimal delay, minimal energy and maximize throughput is a big challenge. Software Defined Networking (SDN) is new paradigm to improve overall network lifetime. It incorporates dynamic changes with minimal end-end delay, and enhances network intelligence. Along with this, intelligence secure routing is also a major constraint. This paper proposes a novel approach to Energy efficient secured routing protocol for Software Defined Internet of vehicles using Restricted Boltzmann Algorithm. This algorithm is to detect hostile routes with minimum delay, minimum energy and maximum throughput compared with traditional routing protocols.

Internet of Vehicles are the important part of Intelligence Traffic Systems (ITS), which are essential for the national security and economy development. The impact assessment for cyberattacks in the IoV protection is of great theoretical and practical significance. Most of the researchers in this field pay attention on the attack impact on a vehicle, and the seldom investigate the impact on the whole system which combines all the vehicles as a whole integration. To tackle this problem, a cyber-physical interaction model based impact assessment of cyberattacks is presented. In this approach, the operation of IoV is modeled from the cyberphysical interaction perspective, and then the propagating process from cyber layer to physical layer is investigated. Based on above model, the impact assessment of cyberattacks on IoV is realized quantitatively. Finally, a simulation on an IoV is conducted to verify the effectiveness of this approach.

Internet of Things (IoT) is becoming increasingly important to intelligent transportation system stakeholders, including cloud-based vehicular cloud (VC) and internet of vehicles (IoV) paradigms. This new trend involves communication and data exchange between several objects within different layers of control. Security in such a deployment is pivotal to realize the general IoT-based smart city. However, the evaluation of the degree of security regarding these paradigms remains a challenge. This study aims to discover and identify common security criteria (CSC) from a context-based analysis pattern and later to discuss, compare, and aggregate a conceptual model of CSC impartially. A privacy granularity classification that maintains data confidentiality is proposed alongside the security selection criteria.

Security is a critical issue for new car generation targeting intelligent transportation systems (ITS), involving autonomous and connected vehicles. In this work we designed a low cost CAN probe and defined analysis tools in order to build attack scenarios. We reuse some threats identified by a previous work. Future researches will address new security protocols.

As Vehicular Ad hoc Network (VANET) features random topology and accommodates freely connected nodes, it is important that the cooperation among the nodes exists. This paper proposes a trust model called Implicit Web of Trust in VANET (IWOT-V) to reason out the trustworthiness of vehicles. Such that untrusted nodes can be identified and avoided when we make a decision regarding whom to follow or cooperate with. Furthermore, the performance of Cooperative Intelligent Transport System (C-ITS) applications improves. The idea of IWOT-V is mainly inspired by web page ranking algorithms such as PageRank. Although there does not exist explicit link structure in VANET because of random topology and dynamic connections, social trust relationship among vehicles exists and an implicit web of trust can be derived. To accomplish the derivation, two algorithms are presented, i.e., BayesTrust and VehicleRank. They are responsible for deriving the local and global trust relationships, respectively. The simulation results show that IWOT-V can accurately identify trusted and untrusted nodes if enough local trust information is collected. The performance of IWOT-V affected by five threat models is demonstrated, and the related discussions are also given.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.

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.

Cloud-based cyber-physical systems, like vehicle and intelligent transportation systems, are now attracting much more attentions. These systems usually include large-scale distributed sensor networks covering various components and producing enormous measurement data. Lots of modeling languages are put to use for describing cyber-physical systems or its aspects, bringing contribution to the development of cyber-physical systems. But most of the modeling techniques only focuse on software aspect so that they could not exactly express the whole cloud-based cyber-physical systems, which require appropriate views and tools in its design; but those tools are hard to be used under systemic or object-oriented methods. For example, the widest used modeling language, UML, could not fulfil the above design's requirements by using the foremer's standard form. This paper presents a method designing the cloud-based cyber-physical systems with AADL, by which we can analyse, model and apply those requirements on cloud platforms ensuring QoS in a relatively highly extensible way at the mean time.