Data-centric Misbehavior Detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page

Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network

High reliance on wireless network connectivity makes the vehicular ad hoc network (VANET) vulnerable to several kinds of cyber security threats. Malicious vehicles accessing the network can lead to hazardous situation by disseminating misleading information or data in the network or by performing cyber-attacks. It is a requirement that the information must be originated from the authentic and authorized vehicle and confidentiality must be maintained. In these circumstances, to protect the network from malicious vehicles, reputation system based on beta probability distribution with trust management model has been proposed to differentiate trustworthy vehicles from malicious vehicles. The trust model is based on adaptive neuro fuzzy inference system (ANFIS) which takes trust metrics as input to evaluate the trustworthiness of the vehicles. The simulation platform for the model is in MATLAB. Simulation results show that the vehicles need at least 80% trustworthiness to be considered as a trusted vehicle in the network

SCTSC: A Semicentralized Traffic Signal Control Mode With Attribute-Based Blockchain in IoVs

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordAssisting traffic control is one of the most important applications on the Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy preservation of this information are critical while conflicted with each other in the vehicular communication. In this paper, we propose a semicentralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes such as locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption (CP-ABE) and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical.National Key R&D Program of ChinaNatural Science Foundation of ChinaFundamental Research Funds for the Central Universities of Chin

Design Models for Trusted Communications in Vehicle-to-Everything (V2X) Networks

Intelligent transportation system is one of the main systems which has been developed to achieve safe traffic and efficient transportation. It enables the road entities to establish connections with other road entities and infrastructure units using Vehicle-to-Everything (V2X) communications. To improve the driving experience, various applications are implemented to allow for road entities to share the information among each other. Then, based on the received information, the road entity can make its own decision regarding road safety and guide the driver. However, when these packets are dropped for any reason, it could lead to inaccurate decisions due to lack of enough information. Therefore, the packets should be sent through a trusted communication. The trusted communication includes a trusted link and trusted road entity. Before sending packets, the road entity should assess the link quality and choose the trusted link to ensure the packet delivery. Also, evaluating the neighboring node behavior is essential to obtain trusted communications because some misbehavior nodes may drop the received packets. As a consequence, two main models are designed to achieve trusted V2X communications. First, a multi-metric Quality of Service (QoS)-balancing relay selection algorithm is proposed to elect the trusted link. Analytic Hierarchy Process (AHP) is applied to evaluate the link based on three metrics, which are channel capacity, link stability and end-to-end delay. Second, a recommendation-based trust model is designed for V2X communication to exclude misbehavior nodes. Based on a comparison between trust-based methods, weighted-sum is chosen in the proposed model. The proposed methods ensure trusted communications by reducing the Packet Dropping Rate (PDR) and increasing the end-to-end delivery packet ratio. In addition, the proposed trust model achieves a very low False Negative Rate (FNR) in comparison with an existing model

V2C: A Trust-Based Vehicle to Cloud Anomaly Detection Framework for Automotive Systems

Vehicles have become connected in many ways. They communicate with the cloud and will use Vehicle-to-Everything (V2X) communication to exchange warning messages and perform cooperative actions such as platooning. Vehicles have already been attacked and will become even more attractive targets due to their increasing connectivity, the amount of data they produce and their importance to our society. It is therefore crucial to provide cyber security measures to prevent and limit the impact of attacks.As it is problematic for a vehicle to reliably assess its own state when it is compromised, we investigate how vehicle trust can be used to identify compromised vehicles and how fleet-wide attacks can be detected at an early stage using cloud data. In our proposed V2C Anomaly Detection framework, peer vehicles assess each other based on their perceived behavior in traffic and V2X-enabled interactions, and upload these assessments to the cloud for analysis. This framework consists of four modules. For each module we define functional demands, interfaces and evaluate solutions proposed in literature allowing manufacturers and fleet owners to choose appropriate techniques. We detail attack scenarios where this type of framework is particularly useful in detecting and identifying potential attacks and failing software and hardware. Furthermore, we describe what basic vehicle data the cloud analysis can be based upon

Secured information dissemination and misbehavior detection in VANETs

In a connected vehicle environment, the vehicles in a region can form a distributed network (Vehicular Ad-hoc Network or VANETs) where they can share traffic-related information such as congestion or no-congestion with other vehicles within its proximity, or with a centralized entity via. the roadside units (RSUs). However, false or fabricated information injected by an attacker (or a malicious vehicle) within the network can disrupt the decision-making process of surrounding vehicles or any traffic-monitoring system. Since in VANETs the size of the distributed network constituting the vehicles can be small, it is not difficult for an attacker to propagate an attack across multiple vehicles within the network. Under such circumstances, it is difficult for any traffic monitoring organization to recognize the traffic scenario of the region of interest (ROI). Furthermore, even if we are able to establish a secured connected vehicle environment, an attacker can leverage the connectivity of individual vehicles to the outside world to detect vulnerabilities, and disrupt the normal functioning of the in-vehicle networks of individual vehicles formed by the different sensors and actuators through remote injection attacks (such as Denial of Service (DoS)). Along this direction, the core contribution of our research is directed towards secured data dissemination, detection of malicious vehicles as well as false and fabricated information within the network. as well as securing the in-vehicle networks through improvisation of the existing arbitration mechanism which otherwise leads to Denial of Service (DoS) attacks (preventing legitimate components from exchanging messages in a timely manner). --Abstract, page iv

Reputation systems and secure communication in vehicular networks

A thorough review of the state of the art will reveal that most VANET applications rely on Public Key Infrastructure (PKI), which uses user certificates managed by a Certification Authority (CA) to handle security. By doing so, they constrain the ad-hoc nature of the VANET imposing a frequent connection to the CA to retrieve the Certificate Revocation List (CRL) and requiring some degree of roadside infrastructure to achieve that connection. Other solutions propose the usage of group signatures where users organize in groups and elect a group manager. The group manager will need to ensure that group members do not misbehave, i.e., do not spread false information, and if they do punish them, evict them from the group and report them to the CA; thus suffering from the same CRL retrieval problem. In this thesis we present a fourfold contribution to improve security in VANETs. First and foremost, Chains of Trust describes a reputation system where users disseminate Points of Interest (POIs) information over the network while their privacy remains protected. It uses asymmetric cryptography and users are responsible for the generation of their own pair of public and private keys. There is no central entity which stores the information users input into the system; instead, that information is kept distributed among the vehicles that make up the network. On top of that, this system requires no roadside infrastructure. Precisely, our main objective with Chains of Trust was to show that just by relying on people¿s driving habits and the sporadic nature of their encounters with other drivers a successful reputation system could be built. The second contribution of this thesis is the application simulator poiSim. Many¿s the time a new VANET application is presented and its authors back their findings using simulation results from renowned networks simulators like ns-2. The major issue with network simulators is that they were not designed with that purpose in mind and handling simulations with hundreds of nodes requires a massive processing power. As a result, authors run small simulations (between 50 and 100 nodes) with vehicles that move randomly in a squared area instead of using real maps, which rend unrealistic results. We show that by building tailored application simulators we can obtain more realistic results. The application simulator poiSim processes a realistic mobility trace produced by a Multi-agent Microscopic Traffic Simulator developed at ETH Zurich, which accurately describes the mobility patterns of 259,977 vehicles over regional maps of Switzerland for 24 hours. This simulation runs on a desktop PC and lasts approximately 120 minutes. In our third contribution we took Chains of Trust one step further in the protection of user privacy to develop Anonymous Chains of Trust. In this system users can temporarily exchange their identity with other users they trust, thus making it impossible for an attacker to know in all certainty who input a particular piece of information into the system. To the best of our knowledge, this is the first time this technique has been used in a reputation system. Finally, in our last contribution we explore a different form of communication for VANETs. The vast majority of VANET applications rely on the IEEE 802.11p/Wireless Access in Vehicular Environments (WAVE) standard or some other form of radio communication. This poses a security risk if we consider how vulnerable radio transmission is to intentional jamming and natural interferences: an attacker could easily block all radio communication in a certain area if his transmitter is powerful enough. Visual Light Communication (VLC), on the other hand, is resilient to jamming over a wide area because it relies on visible light to transmit information and ,unlike WAVE, it has no scalability problems. In this thesis we show that VLC is a secure and valuable form of communication in VANETs