Abstract

Abstract. We introduce the problem of Model Repair for Probabilistic Systems as follows. Given a probabilistic system M and a probabilistic temporal logic formula φ such that M fails to satisfy φ, the Model Repair problem is to find an M that satisfies φ and differs from M only in the transition flows of those states in M that are deemed controllable. Moreover, the cost associated with modifying M 's transition flows to obtain M should be minimized. Using a new version of parametric probabilistic model checking, we show how the Model Repair problem can be reduced to a nonlinear optimization problem with a minimal-cost objective function, thereby yielding a solution technique. We demonstrate the practical utility of our approach by applying it to a number of significant case studies, including a DTMC reward model of the Zeroconf protocol for assigning IP addresses, and a CTMC model of the highly publicized Kaminsky DNS cache-poisoning attack.

Abstract

WC propose Concurrent Transaction Logic (C7X) as the language for specifying, analyzing, and scheduling of workflows. We show that both local and global properties of worktlows can be naturally represented as C7X formulas and reasoning can be done with the use of the proof theory and the semantics of this logic, We describe a transformation that leads to an eilicicnt algorithm for scheduling worldlows in the presencc of global temporal constraints, which leads to decision proccdurcs for dealing with several safety related properties such as whether every valid execution of the workflow satisfits a particular property or whether a worlcfiow execution is consistent with some given global constraints on the ordering of events in a workflow. We also provide tight complexity results on the running times of these algorithms.

Abstract. The focus of contemporary Web information retrieval systems has been to provide efficient support for the querying and retrieval of relevant documents. More recently, information retrieval over semantic metadata extracted from the Web has received an increasing amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this metadata is an interesting and challenging research topic. Just as ranking of documents is a critical component of today's search engines, the ranking of complex relationships will be an important component in tomorrow's Semantic Web analytics engines. Building upon our recent work on specifying and discovering complex relationships in RDF data, called Semantic Associations, we present a flexible ranking approach which can be used to identify more interesting and relevant relationships in the Semantic Web. Additionally, we demonstrate our ranking scheme's effectiveness through an empirical evaluation over a real-world dataset.

Vulnerability analysis is concerned with the problem of identifying weaknesses in computer systems that can be exploited to compromise their security. In this paper we describe a new approach to vulnerability analysis based on model checking. Our approach involves: £ Formal specification of desired security properties. An example of such a property is "no ordinary user can overwrite system log files." £ An abstract model of the system that captures its security-related behaviors. This model is obtained by composing models of system components such as the file system, privileged processes, etc. £ A verification procedure that checks whether the abstract model satisfies the security properties, and if not, produces execution sequences (also called exploit scenarios) that lead to a violation of these properties. An important benefit of a model-based approach is that it can be used to detect known and as-yet-unknown vulnerabilities. This capability contrasts with previous approaches (such as those used in COPS and SATAN) which mainly address known vulnerabilities. This paper demonstrates our approach by modelling a simplified version of a UNIX-based system, and analyzing this system using model-checking techniques to identify nontrivial vulnerabilities. A key contribution of this paper is to show that such an automated analysis is feasible in spite of the fact that the system models are infinite-state systems. Our techniques exploit some of the latest techniques in model-checking, such as constraint-based (implicit) representation of state-space, together with domain-specific optimizations that are appropriate in the context of vulnerability analysis. Clearly, a realistic UNIX system is much more complex than the one that we have modelled in this paper. Nevertheless, we believe that our results show automated and systematic vulnerability analysis of realistic systems to be feasible in the near future, as model-checking techniques continue to improve.

Public and private organizations have access to vast amount of internal, deep Web and open Web information. Transforming this heterogeneous and distributed information into actionable and insightful information is the key to the emerging new class of business intelligence and national security applications. Although role of semantics in search and integration has been often talked about, in this paper we discussed semantic approaches to support analytics on vast amount of heterogeneous data. In particular, we bring together novel academic research and commercialized Semantic Web technology. The academic research related to semantic association identification, is built upon commercial Semantic Web technology for semantic metadata extraction. A prototypical demonstration of this research and technology is presented in the context of an aviation security application of significance to national security.

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