On the Evaluation of Three Pre-Injection Analysis Techniques for Model-Implemented 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. Index Terms—fault injection, attack injection, cybersecurity testing, pre-injection analysis