Moeller, M;
Ferreira, T;
Lu, T;
Foster, N;
Silva, A;
(2025)
Active Learning of Symbolic NetKAT Automata.
Proceedings of the ACM on Programming Languages
, 9
(PLDI)
pp. 1119-1142.
10.1145/3729295.
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Abstract
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 techniques for automatically learning NetKAT models of unknown networks using active learning. Prior work has explored active learning for a wide range of automata (e.g., deterministic, register, Büchi, timed etc.) and also developed applications, such as validating implementations of network protocols. We present algorithms for learning different types of NetKAT automata, including symbolic automata proposed in recent work. We prove the soundness of these algorithms, build a prototype implementation, and evaluate it on a standard benchmark. Our results highlight the applicability of symbolic NetKAT learning for realistic network configurations and topologies.
| Type: | Article |
|---|---|
| Title: | Active Learning of Symbolic NetKAT Automata |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3729295 |
| Publisher version: | https://doi.org/10.1145/3729295 |
| Language: | English |
| Additional information: | © 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210479 |
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