Abstraction and Learning for Infinite-State Compositional Verification

Despite many advances that enable the application of model checking techniques to the verification of large systems, the state-explosion problem remains the main challenge for scalability. Compositional verification addresses this challenge by decomposing the verification of a large system into the verification of its components. Recent techniques use learning-based approaches to automate compositional verification based on the assume-guarantee style reasoning. However, these techniques are only applicable to finite-state systems. In this work, we propose a new framework that interleaves abstraction and learning to perform automated compositional verification of infinite-state systems. We also discuss the role of learning and abstraction in the related context of interface generation for infinite-state components.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455

MaxLength considered harmful to the RPKI

User convenience and strong security are often at odds, and most security applications need to find some sort of balance between these two (often opposing) goals. The Resource Public Key Infrastructure (RPKI), a security infrastructure built on top of interdomain routing, is not immune to this issue. The RPKI uses the maxLength attribute to reduce the amount of information that must be explicitly recorded in its cryptographic objects. MaxLength also allows operators to easily reconfigure their networks without modifying their RPKI objects. Our network measurements, however, suggest that the maxLength attribute strikes the wrong balance between security and user convenience. We therefore believe that operators should avoid using maxLength. We give operational recommendations and develop software that allow operators to reap many of the benefits of maxLength without its security costs.https://eprint.iacr.org/2016/1015.pdfhttps://eprint.iacr.org/2016/1015.pdfPublished versio

Evoplex: A platform for agent-based modeling on networks

Agent-based modeling and network science have been used extensively to advance our understanding of emergent collective behavior in systems that are composed of a large number of simple interacting individuals or agents. With the increasing availability of high computational power in affordable personal computers, dedicated efforts to develop multi-threaded, scalable and easy-to-use software for agent-based simulations are needed more than ever. Evoplex meets this need by providing a fast, robust and extensible platform for developing agent-based models and multi-agent systems on networks. Each agent is represented as a node and interacts with its neighbors, as defined by the network structure. Evoplex is ideal for modeling complex systems, for example in evolutionary game theory and computational social science. In Evoplex, the models are not coupled to the execution parameters or the visualization tools, and there is a user-friendly graphical interface which makes it easy for all users, ranging from newcomers to experienced, to create, analyze, replicate and reproduce the experiments.Comment: 6 pages, 5 figures; accepted for publication in SoftwareX [software available at https://evoplex.org