Active Learning of Symbolic NetKAT Automata

2025-04-18

AI Summary

This work addresses the challenge of modeling unknown network behaviors. We propose the first active learning method for symbolic NetKAT automata, enabling automated modeling and formal verification of real-world network configurations and topologies. Our approach extends the classical query-based active learning framework to the symbolic NetKAT semantic space, uniformly supporting multiple NetKAT semantic variants and providing a rigorous soundness proof. Technically, it integrates symbolic automata theory, NetKAT syntactic and semantic constraints, and an executable prototype toolchain. Evaluated on standard network benchmarks, our method successfully infers high-complexity forwarding policies. Empirical results demonstrate strong practical applicability, modeling accuracy, and scalability—validating its effectiveness for real-world network analysis and verification.

Technology Category

• Machine Learning: Active Learning
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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, B""uchi, 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.

Problem

• Automatically learning NetKAT models of unknown networks
• Developing algorithms for learning symbolic NetKAT automata
• Validating applicability for realistic network configurations

Innovation

• Active learning for NetKAT automata models
• Symbolic automata learning algorithms
• Prototype evaluation on network benchmarks