Biblio

We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software debugger, a user can continue or or halt an episode at any time step and inspect parameters and probability distributions of interest. The system enables insight into the structure of a given policy and in the behavior of a policy in edge cases. We demonstrate the system with a network intrusion use case. We examine the evolution of an IT infrastructure’s state and the actions prescribed by security policies while an attack occurs. The policies for the demonstration have been obtained through a reinforcement learning approach that includes a simulation system where policies are incrementally learned and an emulation system that produces statistics that drive the simulation runs.

Probabilistic model checking is a formal verification technique that has been applied successfully in a variety of domains, providing identification of system errors through quantitative verification of stochastic system models. One domain that can benefit from probabilistic model checking is cloud computing, which must provide highly reliable and secure computational and storage services to large numbers of mission-critical software systems. For real-world domains like cloud computing, external system factors and environmental changes must be estimated accurately in the form of probabilities in system models; inaccurate estimates for the model probabilities can lead to invalid verification results. To address the effects of uncertainty in probability estimates, in previous work we have developed a variety of techniques for perturbation analysis of discrete- and continuous-time Markov chains (DTMCs and CTMCs). These techniques determine the consequences of the uncertainty on verification of system properties. In this paper, we present the first approach for perturbation analysis of Markov decision processes (MDPs), a stochastic formalism that is especially popular due to the significant expressive power it provides through the combination of both probabilistic and nondeterministic choice. Our primary contribution is a novel technique for efficiently analyzing the effects of perturbations of model probabilities on verification of reachability properties of MDPs. The technique heuristically explores the space of adversaries of an MDP, which encode the different ways of resolving the MDP’s nondeterministic choices. We demonstrate the practical effectiveness of our approach by applying it to two case studies of cloud systems.

Enhancing the security and resilience of interdependent infrastructures is crucial. In this paper, we establish a theoretical framework based on Markov decision processes (MDPs) to design optimal resiliency mechanisms for interdependent infrastructures. We use MDPs to capture the dynamics of the failure of constituent components of an infrastructure and their cyber-physical dependencies. Factored MDPs and approximate linear programming are adopted for an exponentially growing dimension of both state and action spaces. Under our approximation scheme, the optimally distributed policy is equivalent to the centralized one. Finally, case studies in a large-scale interdependent system demonstrate the effectiveness of the control strategy to enhance the network resilience to cascading failures.