A Temporal Logic for Hyperproperties

Hyperproperties, as introduced by Clarkson and Schneider, characterize the correctness of a computer program as a condition on its set of computation paths. Standard temporal logics can only refer to a single path at a time, and therefore cannot express many hyperproperties of interest, including noninterference and other important properties in security and coding theory. In this paper, we investigate an extension of temporal logic with explicit path variables. We show that the quantification over paths naturally subsumes other extensions of temporal logic with operators for information flow and knowledge. The model checking problem for temporal logic with path quantification is decidable. For alternation depth 1, the complexity is PSPACE in the length of the formula and NLOGSPACE in the size of the system, as for linear-time temporal logic

Hybrid Statistical Estimation of Mutual Information for Quantifying Information Flow

International audienceAnalysis of a probabilistic system often requires to learn the joint probability distribution of its random variables. The computation of the exact distribution is usually an exhaustive precise analysis on all executions of the system. To avoid the high computational cost of such an exhaustive search, statistical analysis has been studied to efficiently obtain approximate estimates by analyzing only a small but representative subset of the system's behavior. In this paper we propose a hybrid statistical estimation method that combines precise and statistical analyses to estimate mutual information and its confidence interval. We show how to combine the analyses on different components of the system with different precision to obtain an estimate for the whole system. The new method performs weighted statistical analysis with different sample sizes over different components and dynamically finds their optimal sample sizes. Moreover it can reduce sample sizes by using prior knowledge about systems and a new abstraction-then-sampling technique based on qualitative analysis. We show the new method outperforms the state of the art in quantifying information leakage

Discovering, quantifying, and displaying attacks

In the design of software and cyber-physical systems, security is often perceived as a qualitative need, but can only be attained quantitatively. Especially when distributed components are involved, it is hard to predict and confront all possible attacks. A main challenge in the development of complex systems is therefore to discover attacks, quantify them to comprehend their likelihood, and communicate them to non-experts for facilitating the decision process. To address this three-sided challenge we propose a protection analysis over the Quality Calculus that (i) computes all the sets of data required by an attacker to reach a given location in a system, (ii) determines the cheapest set of such attacks for a given notion of cost, and (iii) derives an attack tree that displays the attacks graphically. The protection analysis is first developed in a qualitative setting, and then extended to quantitative settings following an approach applicable to a great many contexts. The quantitative formulation is implemented as an optimisation problem encoded into Satisfiability Modulo Theories, allowing us to deal with complex cost structures. The usefulness of the framework is demonstrated on a national-scale authentication system, studied through a Java implementation of the framework.Comment: LMCS SPECIAL ISSUE FORTE 201

Model based security guarantees and change

Achieving security in practical systems is a hard task. As it is the case for other critical system properties (i.e. safety), security should be a concern through all the phases of software development, starting with the very early phases of requirements and design, because of the potential impact of unwanted behaviour. Moreover, it remains a critical concern throughout a system's life-span, because functionality driven updates or re-engineering of a system can have an impact on its security. The cost of using formal methods is clearly justified for critical applications. But in the context of a wider class of industrial applications answers to two questions are important: What are the gains and limitations of light-weight formal security guarantees achieved at different abstraction levels? What are the advantages of those techniques for reasoning about change? For the first question, we discuss different detailed modelling techniques, ranging from UML models to CPU cache modelling at the level of binary code. To tackle the second question, we discuss results on compositionality and incremental verification techniques which, besides being useful tools for verification in general, allow re-utilization of existing verification results in case of changes in the models. We apply these techniques to exemplary security properties with focus on confidentiality, and pin down security assumptions and guarantees of information flow control across levels of abstraction

About compositional analysis of pi-calculus processes

We set up a logical framework for the compositional analysis of finite pi-calculus processes. In particular, we extend the partial model checking techniques developed for value passing process algebras to a nominal calculus, i.e. the pi-calculus. The logic considered is an adaptation of the ambient logic to the pi-calculus. As one of the possible applications, we show that our techniques may be used to study interesting security properties as confidentiality for (finite) pi-calculus processes

Hybrid Statistical Estimation of Mutual Information for Quantifying Information Flow

Analysis of a probabilistic system often requires to learn the joint probability distribution of its random variables. The computation of the exact distribution is usually an exhaustive precise analysis on all executions of the system. To avoid the high computational cost of such an exhaustive search, statistical analysis has been studied to efficiently obtain approximate estimates by analyzing only a small but representative subset of the system's behavior. In this paper we propose a hybrid statistical estimation method that combines precise and statistical analyses to estimate mutual information and its confidence interval. We show how to combine the analyses on different components of the system with different precision to obtain an estimate for the whole system. The new method performs weighted statistical analysis with different sample sizes over different components and dynamically finds their optimal sample sizes. Moreover it can reduce sample sizes by using prior knowledge about systems and a new abstraction-then-sampling technique based on qualitative analysis. We show the new method outperforms the state of the art in quantifying information leakage