Denial of Service Attack in VANET: A Survey

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Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified

A survey on vehicular communication for cooperative truck platooning application

Platooning is an application where a group of vehicles move one after each other in close proximity, acting jointly as a single physical system. The scope of platooning is to improve safety, reduce fuel consumption, and increase road use efficiency. Even if conceived several decades ago as a concept, based on the new progress in automation and vehicular networking platooning has attracted particular attention in the latest years and is expected to become of common implementation in the next future, at least for trucks.The platoon system is the result of a combination of multiple disciplines, from transportation, to automation, to electronics, to telecommunications. In this survey, we consider the platooning, and more specifically the platooning of trucks, from the point of view of wireless communications. Wireless communications are indeed a key element, since they allow the information to propagate within the convoy with an almost negligible delay and really making all vehicles acting as one. Scope of this paper is to present a comprehensive survey on connected vehicles for the platooning application, starting with an overview of the projects that are driving the development of this technology, followed by a brief overview of the current and upcoming vehicular networking architecture and standards, by a review of the main open issues related to wireless communications applied to platooning, and a discussion of security threats and privacy concerns. The survey will conclude with a discussion of the main areas that we consider still open and that can drive future research directions.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Simulator of Distributed Datasets for Pulse-wave DDoS Attacks

The ever increasing scale and frequency of Distributed Denial-of-Service (DDoS) attacks, as well as the emergence of new forms of attacks, such as pulse-wave DDoS attacks, highlights the importance of ensuring that mitigation capabilities are able to keep up with the escalating threat posed by DDoS attacks. To that end, much work has been done with regard to the generation of DDoS datasets which form the basis for developing effective mitigation tools such as Intrusion Detection Systems (IDS). However, existing datasets typically represent a single, victim-centric viewpoint, which has limitations compared to a distributed dataset that provides multiple different perspectives onto an attack. Thus, this thesis implements a simulator for distributed datasets specifically focused on pulse-wave DDoS attacks, for which at current no datasets are publicly available. The simulator provides high flexibility and configurability in the types of use cases that can be modeled, allowing for the creation of different topologies and attack compositions. The evaluation demonstrates the tool’s capability to create of a wide range of diverse datasets that exhibit different characteristics with regard to metrics that are commonly used in a DDoS attack’s fingerprint. As such, this thesis represents a significant step towards enabling a better understanding of pulse-wave DDoS attacks and thereby the development of improved tools to help defend against them

Attacks on self-driving cars and their countermeasures : a survey

Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-Attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-Attack. We also provide further research directions to improve the security issues associated with self-driving cars. © 2013 IEEE

Modeling a Consortium-based Distributed Ledger Network with Applications for Intelligent Transportation Infrastructure

Emerging distributed-ledger networks are changing the landscape for environments of low trust among participating entities. Implementing such technologies in transportation infrastructure communications and operations would enable, in a secure fashion, decentralized collaboration among entities who do not fully trust each other. This work models a transportation records and events data collection system enabled by a Hyperledger Fabric blockchain network and simulated using a transportation environment modeling tool. A distributed vehicle records management use case is shown with the capability to detect and prevent unauthorized vehicle odometer tampering. Another use case studied is that of vehicular data collected during the event of an accident. It relies on broadcast data collected from the Vehicle Ad-hoc Network (VANET) and submitted as witness reports from nearby vehicles or road-side units who observed the event taking place or detected misbehaving activity by vehicles involved in the accident. Mechanisms for the collection, validation, and corroboration of the reported data which may prove crucial for vehicle accident forensics are described and their implementation is discussed. A performance analysis of the network under various loads is conducted with results suggesting that tailored endorsement policies are an effective mechanism to improve overall network throughput for a given channel. The experimental testbed shows that Hyperledger Fabric and other distributed ledger technologies hold promise for the collection of transportation data and the collaboration of applications and services that consume it