Reasoning about Secure Interoperation Using Soft Constraints

Foley

O’Sullivan

2005

JCS

Self Cite

The security of a network configuration is based not just on the security of its individual components and their direct interconnections, but also on the potential for systems to interoperate indirectly across network routes. Such interoperation has been shown to provide the potential for cascading paths that violate security, in a circuitous manner, across a network. In this paper we show how constraint satisfaction provides a natural approach to expressing the necessary constraints to ensure multilevel security across a network configuration. In particular, soft constraints are used to detect and eliminate the cascading network paths that compromise security. Taking this approach results in practical advancements over existing solutions to this problem. In particular, constraint satisfaction highlights the set of all cascading paths, which we can eliminate in polynomial time by breaking a minimal number of system links to ensure security.

Section: Resultsmentioning

confidence: 56%

A soft constraint-based approach to the cascade vulnerability problem

Foley

O’Sullivan

2005

JCS

Self Cite

“…The integrity of a system can then be evaluated by checking how much it satisfies some specified (soft) constraints [14,15]. In a similar manner, by adding constraints on the type of flow permitted or denied among the nodes of a network, a flow analysis can be executed, revealing inter-operation [16] or cascading [17,18] problems.…”

Section: Securitymentioning

confidence: 99%

Semiring-Based Soft Constraints

Concurrency, Graphs and Models

Rossi

2008

Self Cite

Abstract. The semiring-based formalism to model soft constraint has been introduced in 1995 by Ugo Montanari and the authors of this paper. The idea was to make constraint programming more flexible and widely applicable. We also wanted to define the extension via a general formalism, so that all its instances could inherit its properties and be easily compared. Since then, much work has been done to study, extend, and apply this formalism. This papers gives a brief summary of some of these research activities. Before soft constraints: a brief introduction to constraint programmingConstraint programming [1,42,60,74,68] is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The basic idea in constraint programming is that the user states the constraints and a general purpose constraint solver solves them. Constraints are just relations, and a constraint satisfaction problem (CSP) states which relations should hold among the given decision variables. For example, in scheduling activities in a company, the decision variables might be the starting times and the durations of the activities, as well as the resources needed to perform them, and the constraints might be on the availability of the resources and on their use for a limited number of activities at a time.Constraint solvers take a real-world problem, represented in terms of decision variables and constraints, and find an assignment of values to all the variables that satisfies all the constraints. Constraint solvers search the solution space either systematically, as with backtracking or branch and bound algorithms, or use forms of local search which may be incomplete. Systematic methods often interleave search and inference, where inference consists of propagating the

“…The model that is proposed in this paper forms a part of our ongoing research in using constraint solving techniques as a practical approach for reasoning about security [1,[3][4][5]31]. Building on the results in [4] we demonstrate in this paper that determining whether a particular system configuration meets a quality of protection measure can be described as a constraint satisfaction problem.…”

Section: Introductionmentioning

confidence: 86%

Multilevel Security and Quality of Protection

Foley

O’Sullivan

et al.

Quality of Protection

Self Cite

Abstract. Constraining how information may flow within a system is at the heart of many protection mechanisms and many security policies have direct interpretations in terms of information flow and multilevel security style controls. However, while conceptually simple, multilevel security controls have been difficult to achieve in practice. In this paper we explore how the traditional assurance measures that are used in the network multilevel security model can be re-interpreted and generalised to provide the basis of a framework for reasoning about the quality of protection provided by a secure system configuration.