This chapter provides an overview of the state of knowledge related to stochastic model-based assessment approaches, which are most commonly used for resiliency evaluation of current computing systems. The chapter first introduces a set of representative surveys developed in recent European projects, and then it provides a deeper description of common techniques used in model-based assessment of resilient systems. The most widely used modelling formalisms are reviewed, with a particular focus on state-based formalisms like Stochastic Petri Nets and its extensions. Techniques used in model construction and solution are also discussed, as well as the different classes of analysis tools and frameworks. The techniques analyzed in the chapter span from largeness avoidance and largeness tolerance techniques to more comprehensive modelling approaches that are integrated in the system’s development and assessment process. Some of these techniques try to cope with system’s complexity by automatically deriving the analysis models from engineering models like UML or AADL. Other approaches attack the complexity issue combining different evaluation methods, exploiting their possible complementarities and synergies. A discussion on the open research challenges in model-based resilience assessment is finally provided in the last part of the chapter, based on the reviewed techniques and on the activities carried out within the AMBER Coordination Action.