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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.

What standardization is needed to ensure that quantum technologies do not pose an unacceptable risk from an automotive cybersecurity perspective? Click here to access the full SAE EDGETM Research Report portfolio.

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

Symbolic code execution is a powerful cybersecurity testing approach that facilitates the systematic exploration of all paths within a program to uncover previously unknown cybersecurity vulnerabilities. ...Symbolic code execution is a powerful cybersecurity testing approach that facilitates the systematic exploration of all paths within a program to uncover previously unknown cybersecurity vulnerabilities. This is achieved through a Satisfiability Modulo Theory (SMT) solver, which operates on symbolic values for program inputs instead of using their concrete counterparts.

The SAE AS2C Standard AS5506C Architecture Analysis and Description Language (AADL) is a modeling language for predictive analysis of real-time software reliant, safety and cybersecurity critical systems that provides both the precision of formal modeling and the tool-agnostic freedom of a text-based representation. ...AADL supports multiple domains of architectural analysis such as timing, latency, resources, safety, scheduling, and cybersecurity. Adventium Labs conducted an exercise to determine the applicability of software engineering practices (e.g., continuous integration (CI), application programming interface (API) sharing, test driven development (TDD)) to the AADL-based Architecture Centric Virtual Integration Process (ACVIP).

While current cybersecurity endeavors in the heavy-duty (HD) vehicle space focus on securing conventional communication technologies such as the controller area network (CAN), there is a notable deficiency in defensive research concerning legacy technologies, particularly those utilized between trucks and trailers. ...To the best of current knowledge, this publication marks the first presentation of cybersecurity defense research on the SAE J1708/J1587 protocol stack.

The paper includes a description of cybersecurity measures to protect the vehicle against malicious attacks.

We also analyze how useful Cybersecurity Assurance Levels (CAL) are in this process and how quantitative as well as qualitative metrics can be utilized as evidence.

Using a wireless medium for tractor-trailer communication will bring new cybersecurity challenges and requirements which requires new development and lifecycle considerations.

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.

As a promising strategic industry group that is rapidly evolving around the world, autonomous vehicle is entering a critical phase of commercialization from demonstration to end markets. The global automotive industry and governments are facing new common topics and challenges brought by autonomous vehicle, such as how to test, assess, and administrate the autonomous vehicle to ensure their safe running in real traffic situations and proper interactions with other road users. Starting from the facts that the way to autonomous driving is the process of a robot or a machine taking over driving tasks from a human. This paper summarizes the main characteristics of autonomous vehicle which are different from traditional one, then demonstrates the limitations of the existing certification mechanism and related testing methods when applied to autonomous vehicle.

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.

Multiple approaches have been created to enhance intra-vehicle communications security over the past three decades since the introduction of the Controller Area Network (CAN) protocol. The twin pair differential-mode communications bus is tremendously robust in the face of interference, yet physical access to the bus offers a variety of potential attack vectors whereby false messages and/or denial of service are achievable. This paper evaluates extensions of a Physical-layer (PHY) common-mode watermark-based authentication technique recently developed to improve authentication on the CAN bus by considering the watermark as a side-channel communications means for high value information. We also propose and analyze higher layer algorithms, with benefits and pitfalls, for employing the watermark as a physical-layer firewall.

To help address the issue of message authentication on the Controller Area Network (CAN) bus, researchers at Virginia Tech and Ford Motor Company have developed a proof-of-concept time-evolving watermark-based authentication mechanism that offers robust, cryptographically controlled confirmation of a CAN message's authenticity. This watermark is injected as a common-mode signal on both CAN-HI and CAN-LO bus voltages and has been proven using a low-cost software-defined radio (SDR) testbed. This paper extends prior analysis on the design and proof-of-concept to consider robustness testing over the range of voltages, both steady state drifts and transients, as are commonly witnessed within a vehicle. Overall performance results, along with a dynamic watermark amplitude control, validate the concept as being a practical near-term approach at improving authentication confidence of messages on the CAN bus.

With the rapid development of connected and autonomous vehicles, more sophisticated automotive systems running large portions of software and implementing a variety of communication interfaces are being developed. The ever-expanding codebase increases the risk for software vulnerabilities, while at the same time the large number of communication interfaces make the systems more susceptible to be targeted by attackers. As such, it is of utmost importance for automotive organizations to identify potential vulnerabilities early and continuously in the development lifecycle in an automated manner. In this paper, we suggest a practical approach for integrating fuzz testing into a Continuous Integration (CI) pipeline for automotive systems. As a first step, we have performed a Threat Analysis and Risk Assessment (TARA) of a general E/E architecture to identify high-risk interfaces and functions.

This paper will provide an Overview of Automotive Cyber Security Standards related to the Vehicle OBD-II Data Link. The OBD-II Connector Attack Tree is described with respect to the SAE J3138 requirements for Intrusive vs. non-Intrusive Services. A proposed test method for SAE J3138 is described including hardware and software scripting. Finally, example test results are reviewed and compared with a potential threat boundary.

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.