A Robust Class of Data Languages and an Application to Learning

TL;DR

This paper introduces session automata, a new automata model for processing data words with fresh data values, offering robustness, logical characterizations, and a learning algorithm, advancing the modeling of protocols with infinite alphabets.

Contribution

The paper presents session automata, a robust automata class with closure properties, decidable inclusion, and a logical framework, along with a learning algorithm for inference.

Findings

Session automata are closed under intersection, union, and complementation.

They admit a symbolic regular representation.

A learning algorithm for session automata is established.

Abstract

We introduce session automata, an automata model to process data words, i.e., words over an infinite alphabet. Session automata support the notion of fresh data values, which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. Session automata have an expressiveness partly extending, partly reducing that of classical register automata. We show that, unlike register automata and their various extensions, session automata are robust: They (i) are closed under intersection, union, and (resource-sensitive) complementation, (ii) admit a symbolic regular representation, (iii) have a decidable inclusion problem (unlike register automata), and (iv) enjoy logical characterizations. Using these results, we establish a learning algorithm to infer session automata through membership and equivalence queries.