Flexible State-Merging for learning (P)DFAs in Python

Conference paper (2016)

Authors

C.A. Hammerschmidt University of Luxembourg

Benjamin Loos University of Luxembourg

Radu State University of Luxembourg

Thomas Engel University of Luxembourg

S.E. Verwer Cyber Security -

Research Group

Cyber Security () (TU Delft)

Machine learning Automaton learning Grammar inference PDFA inference

More Info

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To reference this document use:

http://resolver.tudelft.nl/uuid:d0587eb3-e26a-414d-a4b5-d8f159e9cb9e

Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classication to data scientists, without the need to understand the algorithm itself but rather the limitations of PDFA as a model. With applications of automata learning in diverse elds such as network trac analysis, software engineering and biology, a stratied package opens opportunities for practitioners.