Fault- and attack injection are techniques used to measure dependability attributes of computer systems. An important property of such techniques is their efficiency in exploring the target system's fault- or attack space. As this space is generally very large, pre-injection analysis techniques may be used to effectively explore the space. In this paper, we study two such techniques proposed in the past, namelyinject-on-readandinject-on-write. Furthermore, we propose two new techniques callederror space pruning of signalsanderror space pruning of signals and portsand evaluate their efficiency in reducing the space needed to be explored by injection experiments. These techniques were integrated into MODIFI, a fault- and attack injector targeting Simulink models. To the best of our knowledge, we are the first to evaluate these pre-injection techniques for this kind of injector. The results of our evaluation of 11 Simulink models from the automotive domain and one from the avionics domain, show that the new proposed techniques reduce the fault- and attack space needed to be explored by about 27–49%. Using MODIFI, we then performed injection experiments on two automotive models, as well as an aero engine control model, while elaborating on the results obtained.

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.

In this paper, we briefly survey the research with respect to the security of the connected car, and in particular its in-vehicle network. The aim is to highlight the current state of the research; which are the problems found, and what solutions have been suggested. We have structured our investigation by categorizing the research into the following five categories: problems in the in-vehicle network, architectural security features, intrusion detection systems, honeypots, and threats and attacks. We conclude that even though quite some effort has already been expended in the area, most of it has been directed towards problem definition and not so much towards security solutions. We also highlight a few areas that we believe are of immediate concern.