With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. In this paper, we study and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: 1) from the security perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; 2) from the CPS components perspective, we focus on cyber, physical, and cyber-physical components; and 3) from the CPS systems perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS, and smart cars). The model can be both abstract to show general interactions of components in a CPS application, and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection. With this intensive literature review, we attempt to summarize the state-of-the-art on CPS security, provide researchers with a comprehensive list of references, and also encourage the audience to further explore this emerging field.

https://ieeexplore.ieee.org/ielaam/6488907/8172502/7924372-aam.pdf

Cyber-Physical Systems Security—A Survey

Vehicular networks enable vehicles to generate and broadcast messages in order to improve traffic safety and efficiency. However, due to the nontrusted environments, it is difficult for vehicles to evaluate the credibilities of received messages. In this paper, we propose a decentralized trust management system in vehicular networks based on blockchain techniques. In this system, vehicles can validate the received messages from neighboring vehicles using Bayesian Inference Model. Based on the validation result, the vehicle will generate a rating for each message source vehicle. With the ratings uploaded from vehicles, roadside units (RSUs) calculate the trust value offsets of involved vehicles and pack these data into a “block.” Then, each RSU will try to add their “blocks” to the trust blockchain which is maintained by all the RSUs. By employing the joint proof-of-work (PoW) and proof-of-stake consensus mechanism, the more total value of offsets (stake) is in the block, the easier RSU can find the nonce for the hash function (PoW). In this way, all RSUs collaboratively maintain an updated, reliable, and consistent trust blockchain. Simulation results reveal that the proposed system is effective and feasible in collecting, calculating, and storing trust values in vehicular networks.

Blockchain-Based Decentralized Trust Management in Vehicular Networks

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2017.1291351

Policy and society related implications of automated driving: A review of literature and directions for future research

Privacy and Freedom.

Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another vehicle and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, compared with driving individually, such a platoon-based driving pattern can significantly improve road capacity and energy efficiency. Moreover, with the emerging vehicular ad hoc network (VANET), the performance of a platoon in terms of road capacity, safety, energy efficiency, etc., can be further improved. On the other hand, the physical dynamics of vehicles inside the platoon can also affect the performance of a VANET. Such a complex system can be considered a platoon-based vehicular cyber-physical system (VCPS), which has attracted significant attention recently. In this paper, we present a comprehensive survey on a platoon-based VCPS. We first review the related work of a platoon-based VCPS. We then introduce two elementary techniques involved in a platoon-based VCPS, i.e., the vehicular networking architecture and standards, and traffic dynamics, respectively. We further discuss the fundamental issues in a platoon-based VCPS, including vehicle platooning/clustering, cooperative adaptive cruise control, platoon-based vehicular communications, etc., all of which are characterized by the tightly coupled relationship between traffic dynamics and VANET behaviors. Since system verification is critical to VCPS development, we also give an overview of VCPS simulation tools. Finally, we share our view on some open issues that may lead to new research directions.

/pdf/a-survey-on-platoon-based-vehicular-cyber-physical-systems-4izr6vl5m8.pdf

A Survey on Platoon-Based Vehicular Cyber-Physical Systems

Vehicle automation has been one of the fundamental applications within the field of intelligent transportation systems (ITS) since the start of ITS research in the mid-1980s. For most of this time, it has been generally viewed as a futuristic concept that is not close to being ready for deployment. However, recent development of “self-driving” cars and the announcement by car manufacturers of their deployment by 2020 show that this is becoming a reality. The ITS industry has already been focusing much of its attention on the concepts of “connected vehicles” (United States) or “cooperative ITS” (Europe). These concepts are based on communication of data among vehicles (V2V) and/or between vehicles and the infrastructure (V2I/I2V) to provide the information needed to implement ITS applications. The separate threads of automated vehicles and cooperative ITS have not yet been thoroughly woven together, but this will be a necessary step in the near future because the cooperative exchange of data will provide vital inputs to improve the performance and safety of the automation systems. Thus, it is important to start thinking about the cybersecurity implications of cooperative automated vehicle systems. In this paper, we investigate the potential cyberattacks specific to automated vehicles, with their special needs and vulnerabilities. We analyze the threats on autonomous automated vehicles and cooperative automated vehicles. This analysis shows the need for considerably more redundancy than many have been expecting. We also raise awareness to generate discussion about these threats at this early stage in the development of vehicle automation systems.

Potential Cyberattacks on Automated Vehicles

Safety-critical applications in cooperative vehicular networks require authentication of nodes and messages. Yet, privacy of individual vehicles and drivers must be maintained. Pseudonymity can satisfy both security and privacy requirements. Thus, a large body of work emerged in recent years, proposing pseudonym solutions tailored to vehicular networks. In this survey, we detail the challenges and requirements for such pseudonym mechanisms, propose an abstract pseudonym lifecycle, and give an extensive overview and categorization of the state of the art in this research area. Specifically, this survey covers pseudonym schemes based on public key and identity-based cryptography, group signatures and symmetric authentication. We compare the different approaches, give an overview of the current state of standardization, and identify open research challenges.

Pseudonym Schemes in Vehicular Networks: A Survey

The safety and efficiency of vehicular communications rely on the correctness of the data exchanged between vehicles. In this paper we address the issue of detecting and classifying location spoofing misbehavior using the VeReMi dataset. We propose a framework for a system that uses plausibility checks as a feature vector for machine learning models, used to detect and classify misbehavior. Using KNN and SVM, our results show we can improve the overall detection precision of the plausibility checks used in the feature vectors by over 20%, while maintaining a recall within 5%. We have also proven once a misbehavior has been detected it is possible to classify different types of known misbehavior's. Classifying the misbehavior types allows for more accurate and specific action steps to counteract the attacks, hence improving the ability to recover safety and security in the system.

Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET

User privacy is a requirement for wireless vehicular communications, and a number of privacy protection strategies have already been developed and standardized. In particular, methods relying on the use of temporary pseudonyms and silent periods have proved their ability to confuse attackers who would attempt to track vehicles. In addition to their ability to protect privacy, it is important to ensure that these privacy strategies do not hinder the safety applications which rely on vehicular communications. This paper addresses this concern and presents an experimental analysis of the impact of privacy strategies on Intersection Collision Avoidance (ICA) systems. We simulate traffic scenarios at a road intersection and compare the ability of a collision avoidance system to avoid collisions for different pseudonym change schemes. The privacy level is analyzed, as well as the influence of the duration of the silent period on the safety performance of the ICA system. The results highlight the need to jointly design safety applications and privacy strategies.

/pdf/impact-of-v2x-privacy-strategies-on-intersection-collision-42dn5m9nsy.pdf

Impact of V2X privacy strategies on Intersection Collision Avoidance systems

Cooperative Intelligent Transport Systems (C-ITS) is an ongoing technology that will change our driving experience in the near future. In such systems, vehicles and RoadSide Unit (RSU) cooperate by broadcasting V2X messages over the vehicular network. Safety applications use these data to detect and avoid dangerous situations on time. MisBehavior Detection (MBD) in C-ITS is an active research topic which consists of monitoring data semantics of the exchanged Vehicle-to-X communication (V2X) messages to detect and identify potential misbehaving entities. The detection process consists of performing plausibility and consistency checks on the received V2X messages. If an anomaly is detected, the entity may report it by sending a Misbehavior Report (MBR) to the Misbehavior Authority (MA). The MA will then investigate the event and decide to revoke the sender or not. In this paper, we present a MisBehavior Detection (MBD) simulation framework that enables the research community to develop, test, and compare MBD algorithms. We also demonstrate its capabilities by running example scenarios and discuss their results. Framework For Misbehavior Detection (F 2 MD) is open source and available for free on our github.

/pdf/simulation-framework-for-misbehavior-detection-in-vehicular-1yziogl9nu.pdf

Jonathan Petit

Papers

Potential Cyberattacks on Automated Vehicles

Pseudonym Schemes in Vehicular Networks: A Survey

Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET

Impact of V2X privacy strategies on Intersection Collision Avoidance systems

Simulation Framework for Misbehavior Detection in Vehicular Networks