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The increasing use of software and connectivity in modern vehicles has made cybersecurity an important issue in the automotive industry. ISO 21434 is a standard for automotive cybersecurity engineering that provides guidelines for the development and validation of secure automotive systems. For effective implementation and practical use of ISO 21434, it must be incorporated into existing automotive industry development workflows.
In this paper, we investigate the practical applicability of ISO 21434 in the context of the Security Abstraction Model (SAM), a security modeling approach for the domain-specific modeling language EAST-ADL, and provide insights into the benefits and consequences of this approach. In doing so, we describe the methodological opportunities of integrating ISO 21434 into SAM on the one hand and present a case study illustrating the application of this integrated approach in the development of a secure automotive system on the other hand. Our results suggest that the integration of ISO 21434 into SAM better supports automotive system security in the early development phases and makes it transparent to a wide range of stakeholders. At the same time, it becomes clear that a representation of the interrelationships in the form of a metamodel, in contrast to ISO 21434 in which these are only described textually, significantly improves conceptual understanding and ultimately enables pragmatic usability in industrial development.
The development quality for the control software for autonomous vehicles is rapidly progressing, so that the control units in the field generally perform very reliably. Nevertheless, fatal misjudgments occasionally occur putting people at risk: such as the recent accident in which a Tesla vehicle in Autopilot mode rammed a police vehicle. Since the object recognition software which is a part of the control software is based on machine learning (ML) algorithms at its core, one can distinguish a training phase from a deployment phase of the software. In this paper we investigate to what extent the deployment phase has an impact on the robustness and reliability of the software; because just as traditional, software based on ML degrades with time. A widely known effect is the so-called concept drift: in this case, one finds that the deployment conditions in the field have changed and the software, based on the outdated training data, no longer responds adequately to the current field situation. In a previous research paper, we developed the SafeML approach with colleagues from the University of Hull, where datasets are compared for their statistical distance measures. In doing so, we detected that for simple, benchmark data, the statistical distance correlates with the classification accuracy in the field. The contribution of this paper is to analyze the applicability of the SafeML approach to complex, multidimensional data used in autonomous driving. In our analysis, we found that the SafeML approach can be used for this data as well. In practice, this would mean that a vehicle could constantly check itself and detect concept drift situation early.
Development of reliable systems requires that safety and security concerns are acknowledged during system development. Adding them afterwards is risky as many concerns are missed if not elicited together with the system requirements. Unfortunately, languages for systems engineering, like SysML, typically ignore security and safety forcing development teams to split the work into different formats, languages and tools without easy collaboration, with limited traceability, separate versioning and restricted use of automation that tools can provide. We present a model-based approach targeting automotive that integrates safety and security aspects with other system development practices. This is achieved via a comprehensive domain-specific modeling language that is extendable by language users. We demonstrate this approach with practical examples on how security and safety concerns are recognized along with traditional system design and analysis phases.
Security cannot be implemented into a system retrospectively without considerable effort, so security must be taken into consideration already at the beginning of the system development. The engineering of automotive software is by no means an exception to this rule. For addressing automotive security, the AUTOSAR and EAST-ADL standards for domain-specific system and component modeling provide the central foundation as a start. The EASTADL extension SAM enables fully integrated security modeling for traditional feature-targeted attacks. Due to the COVID-19 pandemic, the number of cyber-attacks has increased tremendously and of these, about 98 percent are based on social engineering attacks. These social engineering attacks exploit vulnerabilities in human behaviors, rather than vulnerabilities in a system, to inflict damage. And these social engineering attacks also play a relevant but nonetheless regularly neglected role for automotive software. The contribution of this paper is a novel modeling concept for social engineering attacks and their criticality assessment integrated into a general automotive software security modeling approach. This makes it possible to relate social engineering exploits with feature-related attacks. To elevate the practical usage, we implemented an integration of this concept into the established, domain-specific modeling tool MetaEdit+. The tool support enables collaboration between stakeholders, calculates vulnerability scores, and enables the specification of security objectives and measures to eliminate vulnerabilities.
Machine learning classifiers for image recognition are prevalent in many applications. We study the problem of finding adversarial examples for such classifiers, i.e., to manipulate the images in such a way that they still look like the original images to a human but are misinterpreted by the classifier. Finding adversarial examples corresponds to a search problem in the image space. We focus on black-box attacks that can only use the original classifier to guide the search. The challenge is not to find adversarial examples, but rather to find them efficiently, ideally in real time. We show two novel methods that increase the efficiency of black-box search algorithms for adversarial examples: The first uses a relevance mask, i.e., a bitmask on the original image that restricts the search to those pixels that appear to be more relevant to the attacked classifier than others. The second exploits the discovery of merge drift, a phenomenon that negatively affects search algorithms that are based on the merging of image candidates. We evaluate both concepts on existing and new algorithms.
SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures
(2020)
In view of the advent of autonomous driving, the automotive industry is longing for adequately trained employees in a wide range of fields; these include not so much the classic automotive engineering and embedded systems topics, but rather software development, robotics, machine learning, statistics and design. As a particularly specific, industry-oriented discipline, automotive software engineering is hardly represented in academic teaching, except for a few AUTOSAR courses. But as a beacon project of digitization, autonomous driving can arouse broad interest among students who are interested in how diverse disciplines can be profitably combined to realize an impressive project. This interest is an excellent chance for both universities and industry to engage in constructive dialogue, to carry out exciting multidisciplinary projects at universities and to meet the needs of industry with graduates who have a relevant and versatile education. This workshop provides an opportunity for both sides to meet, share requirements and experiences and develop a best practice course according to industry requirements and academic feasibility.
Highly connected with the environment via various interfaces, cars have been the focus of malicious cyber attacks for years. These attacks are becoming an increasing burden for a society with growing vehicle autonomization: they are the sword of Damocles of future mobility. Therefore, research is particularly active in the area of vehicle IT security, and in part also in the area of dependability, in order to develop effective countermeasures and to maintain a minimum of one step ahead of hackers. This paper examines the known state-of-the-art security and dependability measures based on a detailed and systematic analysis of published cyber attacks on automotive software systems. The sobering result of the analysis of the cyber attacks with the model-based technique SAM (Security Abstraction Model) and a categorization of the examined attacks in relation to the known security and dependability measures is that most countermeasures against cyber attacks are hardly effective. They either are not applicable to the underlying problem or take effect too late; the intruder has already gained access to a substantial part of the vehicle when the countermeasures apply. The paper is thus contributing to an understanding of the gaps that exist today in the area of vehicle security and dependability and concludes concrete research challenges.
Driverless (autonomous) vehicles will have greater attack potential than any other individual mobility vehicles ever before. Most intelligent vehicles require communication interfaces to the environment, direct connections (e.g., Vehicle-to-X (V2X)) to an Original Equipment Manufacturer (OEM) backend service or a cloud. By connecting to the Internet, which is not only necessary for the infotainment systems, cars could increasingly turn into targets for malware or botnet attacks. Remote control via the Internet by a remote attacker is also conceivable, as has already been impressively demonstrated. This paper examines security modeling for cloud-based remote attacks on autonomous vehicles using a Security Abstraction Model (SAM) for automotive software systems). SAM adds to the early phases of (automotive) software architecture development by explicitly documenting attacks and handling them with security techniques. SAM also provides the basis for comprehensive security analysis techniques, such as the already available Common Vulnerability Scoring System (CVSS) or any other attack assessment system.