Adversarial Models for Deterministic Finite Automata

Kaixuang Zhang, Qinglong Wang, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We investigate a finer-grained understanding of the characteristics of particular deterministic finite automata (DFA). Specifically, we study and identify the transitions of a DFA that are more important for maintaining the correctness of the underlying regular language associated with this DFA. To estimate transition importance, we develop an approach that is similar to the approach widely used to expose the vulnerability of neural networks with the adversarial example problem. In our approach, we propose an adversarial model that reveals the sensitive transitions embedded in a DFA. In addition, we find for a DFA its critical patterns where a pattern is a substring that can be taken as the signature of this DFA. Our defined patterns can be implemented as synchronizing words, which represent the passages from different states to the absorbing state of a DFA. Finally, we validate our study through empirical evaluations by showing that our proposed algorithms can effectively identify important transitions and critical patterns. To our knowledge, this is some of the first work to explore adversarial models for DFAs and is important due to the wide use of DFAs in cyberphysical systems.

Original languageEnglish (US)
Title of host publicationAdvances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings
EditorsCyril Goutte, Xiaodan Zhu
Number of pages13
ISBN (Print)9783030473570
StatePublished - 2020
Event33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020 - Ottawa, Canada
Duration: May 13 2020May 15 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12109 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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