Model-based security testing in IoT systems: : A Rapid Review

Security testing is a challenging and effort-demanding task in IoT scenarios. The heterogeneous devices expose different vulnerabilities that can influence the methods and cost of security testing. Model-based security testing techniques support the systematic generation of test cases for the assessment of security requirements by leveraging the specifications of the IoT system model and of the attack templates.

This paper aims to review the adoption of model-based security testing in the context of IoT, and then provides the first systematic and up-to-date comprehensive classification and analysis of research studies in this topic.

We conducted a systematic literature review analyzing 803 publications and finally selecting 17 primary studies, which satisfied our inclusion criteria and were classified according to a set of relevant analysis dimensions.

We report the state-of-the-art about the used formalisms, the test techniques, the objectives, the target applications and domains; we also identify the targeted security attacks, and discuss the challenges, gaps and future research directions.

Our review represents the first attempt to systematically analyze and classify existing studies on model-based security testing for IoT. According to the results, model-based security testing has been applied in core IoT domains. Models complexity and the need of modeling evolving scenarios that include heterogeneous open software and hardware components remain the most important shortcomings. Our study shows that model-based security testing of IoT applications is a promising research direction. The principal future research directions deal with: extending the existing modeling formalisms in order to capture all peculiarities and constraints of complex and large scale IoT networks; the definition of context-aware and dynamic evolution modeling approaches of IoT entities; and the combination of model-based testing techniques with other security test strategies such as penetration testing or learning techniques for model inference.