Model-based structural and behavioral cybersecurity risk assessment in system designs

Cybersecurity risk assessment has become a critical task in systems development and the operation of complex networked systems. However, current state-of-the-art approaches for detecting vulnerabilities, such as automated security testing or penetration testing, often result in late detection. Thus, there is a growing need for security by design, which involves conducting security-related analyses as early as possible in the system development life cycle. This paper proposes an integrated approach that combines static and dynamic hierarchical model-based security risk assessment. The approach enables early identification of security risks during system design, utilizing various models based on the Unified Modeling Language (UML), with lightweight extensions using profiles and stereotypes to capture security attributes like vulnerabilities and asset values. These security attributes are then used to compute relevant properties, including threat space, possible attack paths, and selected network-based security metrics. To facilitate dynamic security analysis, the UML model is subsequently translated into a deterministic and stochastic Petri net (DSPN). This translation allows for the dynamic analysis and simulation of the system’s state and behavior during an attack, capturing temporal aspects and probabilistic transitions. By representing system components and their interactions as modular Petri nets, the DSPN framework facilitates comprehensive simulation and analysis of possible attack scenarios. This also allows us to estimate time-based security metrics such as the duration required for an attacker to compromise system components. Consequently, this combined approach effectively addresses both static security analysis and dynamic state behavior, providing an integrated understanding of the system’s resilience against cyber threats. A real-world industrial case study illustrates the effectiveness of this approach. The underlying data originates from security assessments performed by Keen Security Labs, which were independently verified by BMW (Cai et al., 2019). Specifically, we present an infotainment system network model as implemented in multiple car models along with corresponding attack and defense models. We then demonstrate how the approach assesses the cybersecurity risk of such in-vehicle networks.