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To build secure systems of road vehicles, the cybersecurity engineering standard ISO21434[11] suggests the evaluation of vulnerabilities throughout engineering process, such as attack path analysis, system requirement stage, software architecture, design, and implementation and testing phases. ...With my analysis and practices, it is appropriate to include the common vulnerabilities that ought to be an integral part of the automotive cybersecurity engineering process. In this paper, the author would like to provide a list of vulnerabilities that might be a suggestion for threat analysis and risk assessment and propose two solutions that may be adopted directly in the V-model for security-relevant software development.

The article also focus on innovative approaches that have recently adopted my many cybersecurity professionals for secured operation of ITS involving block-chain, artificial intelligence, and Machine Learning.

Automotive electronics and enterprise IT are converging and thus open the doors for advanced hacking. With their immediate safety impact, cyberattacks on such systems will endanger passengers. Today, there are various methods of security verification and validation in the automotive industry. However, we realize that vulnerability detection is incomplete and inefficient with classic security testing. In this article, we show how an enhanced Grey-Box Penetration Test (GBPT) needs less test cases while being more effective in terms of coverage and indicating less false positives.

Android applications have historically faced vulnerabilities to man-in-the-middle attacks due to insecure custom SSL/TLS certificate validation implementations. In response, Google introduced the Network Security Configuration (NSC) as a configuration-based solution to improve the security of certificate validation practices. NSC was initially developed to enhance the security of Android applications by providing developers with a framework to customize network security settings. However, recent studies have shown that it is often not being leveraged appropriately to enhance security. Motivated by the surge in vehicular connectivity and the corresponding impact on user security and data privacy, our research pivots to the domain of mobile applications for vehicles. As vehicles increasingly become repositories of personal data and integral nodes in the Internet of Things (IoT) ecosystem, ensuring their security moves beyond traditional issues to one of public safety and trust.

Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.

In agriculture industry, increasing use of Vehicle Internet of Things (IoT), telematics and emerging technologies are resulting in smarter machines with connected solutions. Inter and Intra Communication with vehicle to vehicle and inside vehicle - Electronic Control Unit (ECU) to ECU or ECU (Electronic Control Unit) to sensor, requirement for flow of data increased in-turn resulting in increased need for secure communication. In this paper, we focus on functional verification and validation of secure Controller Area Network (CAN) for intra vehicular communication to establish confidentiality, integrity, authenticity, and freshness of data, supporting safety, advanced automation, protection of sensitive data and IP (Intellectual Property) protection. Network security algorithms and software security processes are the layers supporting to achieve our cause.

By looking into the vehicle-infrastructure cooperation (VIC) which is oriented towards intelligent, networked and integrated development, this paper analyzes and proposes the essence and development direction of Intelligent Vehicle Infrastructure Cooperation Systems (I-VICS). With an in-depth analysis of technologies of core importance to VIC and influence factors that constrain VIC development as a whole, the paper comes up with a technological route for VIC, and identifies a direction for vehicle-infrastructure cooperative development that progresses from primary to intermediate cooperation, then to advanced cooperation, and finally to full-fledged cooperation. Policy recommendations aiming at strengthening top-level design, building an integrated vehicle-infrastructure-cloud platform, expediting independence of key techs, building robust standards and regulations for VIC, enhancing workforce development as well as greater efforts at market promotion are put forward.

In addition, due to the cyber-security risk, the security functions to ensure the integrity of the message has to be considered.

Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and onboard intelligence can may help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise the UGVs. In this paper, we consider a connected UGV platoon under cyber attacks that may disrupt safety and degrade performance. An observer-based resilient control strategy is designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. In addition, each UGV generates both internal and external evaluations based on the platoons performance metrics. A cloud-based trust-based information management system collects these evaluations to detect abnormal UGV platoon behaviors.

Day by day, airports adopt more IoT devices. However, airports are not exempt from possible failures due to malware’s proliferation that can abuse vulnerabilities. Computer criminals can access, corrupt, and extract information from individuals or companies. This paper explains the development of a propagation model, which started with a Delphi process. We discuss the preliminary implications for airports of the simulation model built from the Delphi recommendations.