From the 1 of 5 linked papers with an AI index.
5 papers
A Fast Quantitative Analyzer for NetKAT
Thomas Lu, Qiancheng Fu, Kevin Batz +5
The paper introduces a fast analyzer for quantitative network properties using weighted NetKAT, employing a symbolic data structure and specialized algorithms to efficiently comput…
Weighted NetKAT: A Programming Language For Quantitative Network Verification
Emmanuel Suárez Acevedo, Tiago Ferreira, Kevin Batz +3
We introduce weighted NetKAT, a domain-specific language for modeling and verifying quantitative network properties. The language is parametric on a semiring, enabling the treatmen…
Building Better Environments for Autonomous Cyber Defence
Chris Hicks, Elizabeth Bates, Shae McFadden +12
In November 2025, the authors ran a workshop on the topic of what makes a good reinforcement learning (RL) environment for autonomous cyber defence (ACD). This paper details the kn…
StacKAT: Infinite State Network Verification
Jules Jacobs, Nate Foster, Tobias Kappé +4
We develop StacKAT, a network verification language featuring loops, finite state variables, nondeterminism, and - most importantly - access to a stack with accompanying push and p…
Active Learning of Symbolic NetKAT Automata
Mark Moeller, Tiago Ferreira, Thomas Lu +2
NetKAT is a domain-specific programming language and logic that has been successfully used to specify and verify the behavior of packet-switched networks. This paper develops techn…