Fault- and attack injection are techniques usedto measure dependability attributes of  omputer systems. Animportant property of such techniques is their efficiency inexploring the target system’s fault- or attack space. As this spaceis generally very large, pre-injection analysis techniques may beused to effectively explore the space. In this paper, we studytwo such techniques proposed in the past, namely inject-on-readand inject-on-write. Furthermore, we propose two new techniquescalled error space pruning of signals and error space pruning ofsignals and ports and evaluate their efficiency in reducing thespace needed to be explored by injection experiments. Thesetechniques were integrated into MODIFI, a fault- and attackinjector targeting Simulink models. To the best of our knowledge,we are the first to evaluate these pre-injection techniques for thiskind of injector.The results of our evaluation of 11 Simulink models from theautomotive domain and one from the avionics domain, show thatthe new proposed techniques reduce the fault- and attack spaceneeded to be explored by about 27–49%. Using MODIFI, we thenperformed injection experiments on two automotive models,  swell as an aero engine control model, while elaborating on theresults obtained.

he increased importance of cybersecurity in autonomous machinery is becoming evident in the forestry domain. Forestry worksites are becoming more complex with the involvement of multiple systems and system of systems. Hence, there is a need to investigate how to address cybersecurity challenges for autonomous systems of systems in the forestry domain. Using a literature review and adapting standards from similar domains, as well as collaborative sessions with domain experts, we identify challenges towards CE-certified autonomous forestry machines focusing on cybersecurity and safety. Furthermore, we discuss the relationship between safety and cybersecurity risk assessment and their relation to AI, highlighting the need for a holistic methodology for their assurance.

As vehicles become more and more connected with their surroundings and utilize an increasing number of services, they also become more exposed to threats as the attack surface increases. With increasing attack surfaces and continuing challenges of eliminating vulnerabilities, vehicles need to be designed to work even under malicious activities, i.e., under attacks. In this paper, we present a resilience framework that integrates analysis of safety and cybersecurity mechanisms. We also integrate resilience for safety and cybersecurity into the fault – error – failure chain. The framework is useful for analyzing the propagation of faults and attacks between different system layers. This facilitates identification of adequate resilience mechanisms at different system layers as well as deriving suitable test cases for verification and validation of system resilience using fault and attack injection.

Fault- and attack injection are techniques used to measure dependability attributes of computer systems. An important property of such injectors is their efficiency that deals with the time and effort needed to explore the target system’s fault- or attack space. As this space is generally very large, techniques such as pre-injection analyses are used to effectively explore the space. In this paper, we study two such techniques that have been proposed in the past, namely inject-on-read and inject-on-write. Moreover, we propose a new technique called error space pruning of signals and evaluate its efficiency in reducing the space needed to be explored by fault and attack injection experiments. We implemented and integrated these techniques into MODIFI, a model-implemented fault and attack injector, which has been effectively used in the past to evaluate Simulink models in the presence of faults and attacks. To the best of our knowledge, we are the first to integrate these pre-injection analysis techniques into an injector that injects faults and attacks into Simulink models.The results of our evaluation on 11 vehicular Simulink models show that the error space pruning of signals reduce the attack space by about 30–43%, hence allowing the attack space to be exploited by fewer number of attack injection experiments. Using MODIFI, we then performed attack injection experiments on two of these vehicular Simulink models, a comfort control model and a brake-by-wire model, while elaborating on the results obtained

Embedded electronic systems need to be equipped with different types of security mechanisms to protect themselves and to mitigate the effects of cybersecurity attacks. These mechanisms should be evaluated with respect to their impacts on dependability and security attributes such as availability, reliability, safety, etc. The evaluation is of great importance as, e.g., a security mechanism should never violate the system safety. Therefore, in this paper, we evaluate a comprehensive set of security mechanisms consisting of 17 different types of mechanisms with respect to their impact on dependability and security attributes. The results show that, in general, the use of these mechanisms have positive effect on system dependability and security. However, there are at least three mechanisms that could have negative impacts on system dependability by violating safety and availability requirements. The results support our claim that the analyses such as the ones conducted in this paper are necessary when selecting and implementing an optimal set of safety and security mechanisms.