Search Results

The development of highly automated driving functions (AD) recently rises the demand for so called Fail-Operational systems for native driving functions like steering and braking of vehicles. Fail-Operational systems shall guarantee the availability of driving functions even in presence of failures. This can also mean a degradation of system performance or limiting a system’s remaining operating period. In either case, the goal is independency from a human driver as a permanently situation-aware safety fallback solution to provide a certain level of autonomy. In parallel, the connectivity of modern vehicles is increasing rapidly and especially in vehicles with highly automated functions, there is a high demand for connected functions, Infotainment (web conference, Internet, Shopping) and Entertainment (Streaming, Gaming) to entertain the passengers, who should no longer occupied with driving tasks.

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.

In this paper, we outline past, present and future applications of automotive security for engine ECUs. Electronic immobilizers and anti-tuning countermeasures have been used for several years. Recently, OEMs and suppliers are facing more and more powerful attackers, and as a result, have introduced stronger countermeasures based on hardware security. Finally, with the advent of connected cars, it is expected that many things that currently require a physical connection will be done remotely in a near future. This includes remote diagnostics, reprogramming and engine calibration.

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 underlying systems are susceptible to safety and cybersecurity attacks as the involved ECUs are interconnected. The security attacks can lead to disrupting the safe operation of the vehicle while causing injury to the passengers. ...Consequently, the functional safety requirements and cybersecurity requirements can be aligned with each other. In this article, a case study of the application of the THARA framework is presented through the risk analysis of safety and security threats applicable to the rearview camera (RVC) feature of the vehicle.

IOOs and ADS developers agree that cost, communications, interoperability, cybersecurity, operation, maintenance, and other issues undercut efforts to deploy a comprehensive connected infrastructure.

Less emphasis has been placed to-date on helping ensure cybersecurity of cyber-physical automotive systems. However, this is changing as both the world and the automotive industry become more aware of the potential ramifications of cyber-attacks on vehicles.

., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384.

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

The caveat to these additional capabilities is issues like cybersecurity, complexity, etc. This paper is an exploration into FuSa and CAVs and will present a systematic approach to understand challenges and propose potential framework, Intelligent Vehicle Monitoring for Safety and Security (IVMSS) to handle faults/malfunctions in CAVs, and specifically autonomous systems.

Recent developments in the commercialization of mobility services have brought unprecedented connectivity to the automotive sector. While the adoption of connected features provides significant benefits to vehicle owners, adversaries may leverage zero-day attacks to target the expanded attack surface and make unauthorized access to sensitive data. Protecting new generations of automotive controllers against malicious intrusions requires solutions that do not depend on conventional countermeasures, which often fall short when pitted against sophisticated exploitation attempts. In this paper, we describe some of the latent risks in current automotive systems along with a well-engineered multi-layer defense strategy. Further, we introduce a novel and comprehensive attack and performance test framework which considers state-of-the-art memory corruption attacks, countermeasures and evaluation methods.

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.