Integrating safety and cybersecurity through stochastic model checking
Emerging technologies, like self-driving cars, drones, and the Internet-of-Things must not impose threats to people, neither due to accidental failures (safety), nor due to malicious attacks (security). As historically separated fields, safety and security are often analyzed in isolation. They are, however, heavily intertwined: measures that increase safety often decrease security and vice versa. Also, security vulnerabilities often cause safety hazards, e.g. in autonomous cars. Therefore, for effective decision-making, safety and security must be considered in combination. 

The CAESAR project  will develop an effective framework for the joint analysis of safety and security risks.
The successful integration of safety and security faces three challenges: 
1. The complex interaction between safety and security, mapping how vulnerabilities and failures propagate through a system and lead to disruptions. 
2. The lack of efficient algorithms to compute system-level risk metrics, such as the likelihood and expected damage of disruptions. Such metrics are pivotal to prioritize risks and mitigate them via appropriate countermeasures. 
3. The lack of proper risk quantification methods. Numbers are crucial to devise cost-effective countermeasures. Yet, objective numbers on safety and (especially) security risks are notoriously hard to obtain. 
The CAESAR project will address these challenges by novel combinations of mathematical game theory, stochastic model checking and the Bayesian, fuzzy, and Dempster-Schafer frameworks for uncertainty reasoning. 
Key outcomes: 
•	An effective framework for joint safety-security analysis
•	Scalable algorithms and diagnosis methods to compute safety-security risk metrics
•	Stochastic model checking in the presence of uncertainty
CAESAR will not only yield breakthroughs in safety-security analysis, but also for quantitative analyses in other domains. It will make decision making on safety-security easier, more systematic, and transparent.