Towards alignment of architectural domains in security policy specifications

Large organizations need to align the security architecture across three different domains: access control, network layout and physical infrastructure. Security policy specification formalisms are usually dedicated to only one or two of these domains. Consequently, more than one policy has to be maintained, leading to alignment problems. Approaches from the area of model-driven security enable creating graphical models that span all three domains, but these models do not scale well in real-world scenarios with hundreds of applications and thousands of user roles. In this paper, we demonstrate the feasibility of aligning all three domains in a single enforceable security policy expressed in a Prolog-based formalism by using the Law Governed Interaction (LGI) framework. Our approach alleviates the limitations of policy formalisms that are domain-specific while helping to reach scalability by automatic enforcement provided by LGI

kLog: A Language for Logical and Relational Learning with Kernels

We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph --- in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials

Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm