Synthesis of Optimal Stealthy Attacks against Diagnosability in Labeled Petri Nets

This paper addresses the diagnosability analysis problem under external malicious attacks of a networked discrete event system modeled by labeled Petri net. In particular, we focus on a stealthy replacement attack to alter or corrupt the observation of the system, in which the transition labels are...

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Published inIEEE/CAA journal of automatica sinica Vol. 12; no. 8; pp. 1661 - 1672
Main Authors Liu, Ruotian, Mangini, Agostino Marcello, Fanti, Maria Pia
Format Journal Article
LanguageEnglish
Published Chinese Association of Automation (CAA) 01.08.2025
Subjects
Automata
Costs
Cyber attack
diagnosability
discrete event system (DES)
Discrete-event systems
Europe
Finite element analysis
Firing
Focusing
labeled Petri net (LPN)
Labeling
Optimization
Petri nets
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Abstract This paper addresses the diagnosability analysis problem under external malicious attacks of a networked discrete event system modeled by labeled Petri net. In particular, we focus on a stealthy replacement attack to alter or corrupt the observation of the system, in which the transition labels are replaced by others or empty string, and its attack stealthiness requires that the corrupted observations should be contained in the behavior of system. The aim of this work is, from an attacker viewpoint, to design a stealthy replacement attack for violating the diagnosability of system. To this end, we first build a new structure, called complete unfolded verifier, with the notion of a predefined elementary unsound path that leads to the violation of diagnosability, which is used to enumerate all the potential attacked paths to be transformed into elementary unsound ones. Then an optimal attack synthesis problem in terms of minimum energy cost is formulated by determining whether an elementary unsound path is generated via solving a set of integer nonlinear programming problems. Finally, we show that the nonlinear programming problems can be transformed into integer linear programming problems by introducing additional linear constraints. Examples are used to illustrate the proposed attack strategy.
AbstractList This paper addresses the diagnosability analysis problem under external malicious attacks of a networked discrete event system modeled by labeled Petri net. In particular, we focus on a stealthy replacement attack to alter or corrupt the observation of the system, in which the transition labels are replaced by others or empty string, and its attack stealthiness requires that the corrupted observations should be contained in the behavior of system. The aim of this work is, from an attacker viewpoint, to design a stealthy replacement attack for violating the diagnosability of system. To this end, we first build a new structure, called complete unfolded verifier, with the notion of a predefined elementary unsound path that leads to the violation of diagnosability, which is used to enumerate all the potential attacked paths to be transformed into elementary unsound ones. Then an optimal attack synthesis problem in terms of minimum energy cost is formulated by determining whether an elementary unsound path is generated via solving a set of integer nonlinear programming problems. Finally, we show that the nonlinear programming problems can be transformed into integer linear programming problems by introducing additional linear constraints. Examples are used to illustrate the proposed attack strategy.
Author Liu, Ruotian
Fanti, Maria Pia
Mangini, Agostino Marcello
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Snippet This paper addresses the diagnosability analysis problem under external malicious attacks of a networked discrete event system modeled by labeled Petri net. In...
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SubjectTerms Automata
Costs
Cyber attack
diagnosability
discrete event system (DES)
Discrete-event systems
Europe
Finite element analysis
Firing
Focusing
labeled Petri net (LPN)
Labeling
Optimization
Petri nets
Title Synthesis of Optimal Stealthy Attacks against Diagnosability in Labeled Petri Nets
URI https://ieeexplore.ieee.org/document/11033718
Volume 12
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