Learning Deterministic Finite Automata from Innite Alphabets

Conference paper (2016)

Authors

G. Pellegrino Cyber Security - EEMCS
C.A. Hammerschmidt University of Luxembourg
S.E. Verwer Cyber Security - EEMCS
Q. Lin Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
More Info
expand_more

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

We proposes an algorithm to learn automata innite alphabets, or at least too large to enumerate. We apply it to dene a generic model intended for regression, with transitions
constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the
alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression
where typical models are hard to interpret.