Biblio

Roads are the backbone of our country, they play an important role for human progress. Roads seem to be dangerous and harmful for human beings on hills, near rivers, lakes and small ridges. It's possible with the help of IoT (Internet of things) to incorporate all the things made efficiently and effectively. IoT in combination with roads make daily life smart and excellent. This paper shows IoT technology will be the beginning of smart cities and it will reduce road accidents and collisions. If all vehicles are IoT based and connected with the internet, then an efficient method to guide, it performs urgent action, when less time is available. Internet and antenna technology in combination with IoT perform fully automation in our day-to-day life. It will provide excellent service as well as accuracy and precision.

Cooperative Intelligent Transportation System (CITS) has been introduced recently to increase road safety, traffic efficiency, and to enable various infotainment and comfort applications and services. To this end, a bunch technologies have been deployed to maintain and promote ITS. In essence, ITS is composed of vehicles, roadside infrastructure, and the environment that includes pedestrians, and other entities. Recently, several solutions were suggested to handle with the challenges faced by the vehicular networks (VN) using future internet architectures. One of the promising solutions proposed recently is Vehicular Fog computing (VFC), an attractive solution that supports sensitive service requests considering factors such as latency, mobility, localization, and scalability. VFC also provides a virtual platform for real-time big data analytic using servers or vehicles as a fog infrastructure. This paper surveys the general fog computing (FC) concept, the VFC architectures, and the key characteristics of several intelligent computing applications. We mainly focus on trust and security challenges in VFC deployment and real-time BD analytic in vehicular environment. We identify the faced challenges and future research directions in VFC and we highlight the research gap that can be exploited by researchers and vehicular manufactures while designing a new secure VFC architecture.

Real-time data transmissions from a vehicle enhance road safety and traffic efficiency by aggregating data in a central server for data analytics. When drivers share their instantaneous vehicular information for a service provider to perform a legitimate task, a curious service provider may also infer private information it has not been authorized for. In this paper, we propose a privacy preservation framework based on the Hilbert Schmidt Independence Criterion (HSIC) to sanitize driving data to protect the vehicle’s trajectory from adversarial inference while ensuring the data is still useful for driver behavior detection. We develop a deep learning model to learn the HSIC sanitizer and demonstrate through two datasets that our approach achieves better utility-privacy trade-offs when compared to three other benchmarks.

Connected Autonomous Vehicle (CAV) applications have shown the promise of transformative impact on road safety, transportation experience, and sustainability. However, they open large and complex attack surfaces: an adversary can corrupt sensory and communication inputs with catastrophic results. A key challenge in development of security solutions for CAV applications is the lack of effective infrastructure for evaluating such solutions. In this paper, we address the problem by designing an automated, flexible evaluation infrastructure for CAV security solutions. Our tool, CAVELIER, provides an extensible evaluation architecture for CAV security solutions against compromised communication and sensor channels. The tool can be customized for a variety of CAV applications and to target diverse usage models. We illustrate the framework with a number of case studies for security resiliency evaluation in Cooperative Adaptive Cruise Control (CACC).

Smart cities are a wide range of projects made to facilitate the problems of everyday life and ensure security. Our interest focuses only on the Intelligent Transport System (ITS) that takes care of the transportation issues using the Vehicular Ad-Hoc Network (VANET) paradigm as its base. VANETs are a promising technology for autonomous driving that provides many benefits to the user conveniences to improve road safety and driving comfort. VANET is a promising technology for autonomous driving that provides many benefits to the user's conveniences by improving road safety and driving comfort. The problem with such rapid development is the continuously increasing digital threats. Among all these threats, we will target the Sybil attack since it has been proved to be one of the most dangerous attacks in VANETs. It allows the attacker to generate multiple forged identities to disseminate numerous false messages, disrupt safety-related services, or misuse the systems. In addition, Machine Learning (ML) is showing a significant influence on classification problems, thus we propose a behavior-based classification algorithm that is tested on the provided VeReMi dataset coupled with various machine learning techniques for comparison. The simulation results prove the ability of our proposed mechanism to detect the Sybil attack in VANETs.

Vehicular Ad-hoc Networks (VANETs) is a very fast emerging research area these days due to their contribution in designing Intelligent transportation systems (ITS). ITS is a well-organized group of wireless networks. It is a derived class of Mobile Ad-hoc Networks (MANETs). VANET is an instant-formed ad-hoc network, due to the mobility of vehicles on the road. The goal of using ITS is to enhance road safety, driving comfort, and traffic effectiveness by alerting the drivers at right time about upcoming dangerous situations, traffic jams, road diverted, weather conditions, real-time news, and entertainment. We can consider Vehicular communication as an enabler for future driverless cars. For these all above applications, it is necessary to make a threat-free environment to establish secure, fast, and efficient communication in VANETs. In this paper, we had discussed the overviews, characteristics, securities, applications, and various data dissemination techniques in VANET.

Vehicular networks have been considered as a hopeful technology to enhance road safety, which is a crossing area of Internet of Things (IoT) and Intelligent Transportation Systems (ITS). Current Internet architecture using the TCP/IP model and based on host-to-host is limited when it comes to vehicular communications which are characterized by high speed and dynamic topology. Thus, using Named Data Networking (NDN) in connected vehicles may tackle the issues faced with the TCP/IP model. In this paper, we investigate the security concerns of applying NDN in vehicular environments and discuss the remaining challenges in order to guide researchers in this field to choose their future research direction.

Autonomous vehicles (AVs) offer a wide range of promising benefits by reducing traffic accidents, environmental pollution, traffic congestion and land usage etc. However, to reap the intended benefits of AVs, it is inevitable that this technology should be trusted and accepted by the public. The consumer's substantial trust upon AVs will lead to its widespread adoption in the real-life. It is well understood that the preservation of strong security and privacy features influence a consumer's trust on a product in a positive manner. In this paper, we introduce a novel concept of digital labels for AVs to increase consumers awareness and trust regarding the security level of their vehicle. We present an architecture called Cybersecurity Box (CSBox) that leverages digital labels to display and inform consumers and passengers about cybersecurity status of the AV in use. The introduction of cybersecurity digital labels on the dashboard of AVs would attempt to increase the trust level of consumers and passengers on this promising technology.

In recent years, the Internet-of-Things (IoT)-based traffic management system (ITMS) has attracted the attention of researchers from different fields, such as the automotive industry, academia and traffic management, due to its ability to enhance road safety and improve traffic efficiency. ITMS uses the Vehicle Ad-hoc Network (VANET) to communicate messages about traffic conditions or the event on the route to ensure the safety of the commuter. ITMS uses wireless communication technology for communication between different devices. Wireless communication has challenges to privacy and security. Challenges such as confidentiality, authentication, integrity, non-repudiation, identity, trust are major concerns of either security or privacy or both. This paper discusses the features of the traffic system, the features of the traffic management system (TMS) and the features of IoT that can be used in TMS with its challenges. Further, this paper analyses the work done in the last few years with the future scope of IoT in the TMS.

The development of autonomous connected vehicles, in particular, moving as a platoon formation, has received great attention in recent years. The autonomous movement allows to increase the efficiency of the transportation infrastructure usage, reduce the fuel consumption, improve road safety, decrease traffic congestion, and others. To maintain an optimal spacing policy in a platoon formation, it is necessary to exchange information between vehicles. The Vehicular ad hoc Network (VANET) is the key component to establish wireless vehicle-to-vehicle communications. However, vehicular communications can be affected by different security threats. In this paper, we consider the third-order consensus approach as a control strategy for the vehicle platoon. We investigate several types of malicious attacks (spoofing, message falsification) and propose an anomaly detection algorithm that allows us to detect the malicious vehicle and enhance the security of the vehicle platoon. The experimental study of the proposed approach is conducted using Plexe, a vehicular network simulator that permits the realistic simulation of platooning systems.

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.

This study investigates how risk influences users' trust before and after interactions with technologies such as autonomous vehicles (AVs'). Also, the psychophysiological correlates of users' trust from users” eletrodermal activity responses. Eighteen (18) carefully selected participants embark on a hypothetical trip playing an autonomous vehicle driving game. In order to stay safe, throughout the drive experience under four risk conditions (very high risk, high risk, low risk and no risk) that are based on automotive safety and integrity levels (ASIL D, C, B, A), participants exhibit either high or low trust by evaluating the AVs' to be highly or less trustworthy and consequently relying on the Artificial intelligence or the joystick to control the vehicle. The result of the experiment shows that there is significant increase in users' trust and user's delegation of controls to AVs' as risk decreases and vice-versa. In addition, there was a significant difference between user's initial trust before and after interacting with AVs' under varying risk conditions. Finally, there was a significant correlation in users' psychophysiological responses (electrodermal activity) when exhibiting higher and lower trust levels towards AVs'. The implications of these results and future research opportunities are discussed.

The objective of this study is to explore changes of trust by a situation where drivers need to intervene. Trust in automation is a key determinant for appropriate interaction between drivers and the system. System transparency and types of system failure influence shaping trust in a supervisory control. Subjective ratings of trust were collected to examine the impact of two factors: system transparency (Detailed vs. Less) and system failure (by Limits vs. Malfunction) in a driving simulator study in which drivers experienced a partially automated vehicle. We examined trust ratings at three points: before and after driver intervention in the automated vehicle, and after subsequent experience of flawless automated driving. Our result found that system transparency did not have significant impacts on trust change from before to after the intervention. System-malfunction led trust reduction compared to those of before the intervention, whilst system-limits did not influence trust. The subsequent experience recovered decreased trust, in addition, when the system-limit occurred to drivers who have detailed information about the system, trust prompted in spite of the intervention. The present finding has implications for automation design to achieve the appropriate level of trust.

The continuous progress of electronic equipments has influenced car manufacturers, leading to the integration of the latest infotainment technologies and providing connection to external devices, such as mobile phones. Modern cars work with ECUs (Electronic Control Units) that handle user interactions and sensor data, by also sending information to actuators using simple, reliable and efficient networks with fast protocols, like CAN (Controller Area Network). This is the most used vehicular protocol, which allows interconnecting different ECUs, making them interact in a synergic manner. On the down side, there is a security risk related to the exposition of malicious ECU's frames-possibly generated by compromised devices-which can lead to the possibility to remote control all the car equipments (like brakes and others) by an attacker. We propose a solution to this problem, designing an authentication and encryption system above CAN, called AuthentiCAN. Our proposal is tailored for the evolution of CAN called CAN-FD, and avoids the possibility for an attacker to inject malicious frames that are not discarded by the destination ECUs. Also, we avoid the possibility for an attacker to learn the interactions that occur across ECUs, with the objective of maliciously replaying messages-which would lead the actuator's logic to be no longer compliant with the actual data sources. We also present a simulation study of our solution, where we provide an assessment of its overhead, e.g. in terms of reduction of the throughput of data-unit transfer over CAN-FD, caused by the added security features.

Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack detection scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object detection technique is used to accurately identify nearby objects for Sybil attack detection and the multi-step verification process minimizes the false positive of the detection.

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.

VANET is an emerging technology incorporating ad hoc network to accomplish intelligent communications between vehicles, improvement in road traffic efficiency and safety. In some situations movement of vehicles is in a certain range, over particular distance or just in a specific tendency. Such a network can be called as incompletely or partially predictable network. An efficient use of such network, position and motion of nodes as well as relative history in big data is an open issue in vehicular ad hoc network. A hybrid protocol which provides secure and trustworthiness evaluation based routing can be used in VANET. Here Secure Trustworthiness Evaluation Based Routing Protocol is implemented using NS2 software. Its performance is very good in terms of the Average End to End Delay, Packet Delivery Ratio and Normalized Routing Overhead.

The evolution of the Internet of Vehicles (IoV) paradigm has recently attracted a lot of researchers and industries. Vehicular Ad Hoc Networks (VANET) is the networking model that lies at the heart of this technology. It enables the vehicles to exchange relevant information concerning road conditions and safety. However, ensuring communication security has been and still is one of the main challenges to vehicles' interconnection. To secure the interconnected vehicular system, many cryptography techniques, communication protocols, and certification and reputation-based security approaches were proposed. Nonetheless, some limitations are still present, preventing the practical implementation of such approaches. In this paper, we first define a set of locally-perceived behavioral reputation parameters that enable a distributed evaluation of vehicles' reputation. Then, we integrate these parameters into the design of a reputation management system to exclude malicious or faulty vehicles from the IoV network. Our system can help in the prevention of several attacks on the VANET environment such as Sybil and Denial of Service attacks, and can be implemented in a fully decentralized fashion.

Vehicular ad hoc networks (VANETs) are designed to provide traffic safety by enabling vehicles to broadcast information-such as speed, location and heading-through inter-vehicular communications to proactively avoid collisions. However, the attacks targeting these networks might overshadow their advantages if not protected against. One powerful threat against VANETs is vehicular botnets. In our earlier work, we demonstrated several vehicular botnet attacks that can have damaging impacts on the security and privacy of VANETs. In this paper, we present SHIELDNET, the first detection mechanism against vehicular botnets. Similar to the detection approaches against Internet botnets, we target the vehicular botnet communication and use several machine learning techniques to identify vehicular bots. We show via simulation that SHIELDNET can identify 77 percent of the vehicular bots. We propose several improvements on the VANET standards and show that their existing vulnerabilities make an effective defense against vehicular botnets infeasible.

Road accidents are challenging threat in the present scenario. In India there are 5, 01,423 road accidents in 2015. A day 400 hundred deaths are forcing to India to take car safety sincerely. The common cause for road accidents is driver's distraction. In current world the people are dominated by the tablet PC and other hand held devices. The VANET technology is a vehicle-to-vehicle communication; here the main challenge will be to deliver qualified communication during mobility. The paper proposes a standard new restricted lightweight authentication protocol utilizing key agreement theme for VANETs. Inside the planned topic, it has three sorts of validations: 1) V2V 2) V2CH; and 3) CH and RSU. Aside from this authentication, the planned topic conjointly keeps up mystery keys between RSUs for the safe communication. Thorough informal security analysis demonstrates the planned subject is skilled to guard different malicious attack. In addition, the NS2 Simulation exhibits the possibility of the proposed plan in VANET background.

This paper addresses the fundamental problem of identifying driver talking directions using a single smartphone, which can help drivers by warning distraction of having conversations with passengers in a vehicle and enable safety enhancement. The basic idea of our system is to perform talking status and direction identification using two microphones on a smartphone. We first use the sound recorded by the two microphones to identify whether the driver is talking or not. If yes, we then extract the so-called channel fingerprint from the speech signal and classify it into one of three typical driver talking directions, namely, front, right and back, using a trained model obtained in advance. The key novelty of our scheme is the proposition of channel fingerprint which leverages the heavy multipath effects in the harsh in-vehicle environment and cancels the variability of human voice, both of which combine to invalidate traditional TDoA, DoA and fingerprint based sound source localization approaches. We conducted extensive experiments using two kinds of phones and two vehicles for four phone placements in three representative scenarios, and collected 23 hours voice data from 20 participants. The results show that our system can achieve 95.0% classification accuracy on average.

With the growing number of electronic features in cars and their connections to the cloud, smartphones, road-side equipment, and neighboring cars the need for effective cybersecurity is paramount. Beyond the concern of brand degradation, warranty fraud, and recalls, what keeps manufacturers up at night is the threat of malicious attacks which can affect the safety of vehicles on the road. Would any single protection technique provide the security needed over the long lifetime of a vehicle? We present a new methodology for automotive cybersecurity where the designs are made to withstand attacks in the future based on the concepts of high availability and malicious fault-tolerance through self-defending techniques. When a system has an intrusion, self-defending technologies work to contain the breach using integrity verification, self-healing, and fail-over techniques to keep the system running.