Sensor Deception Attacks Against Initial-State Privacy in Supervisory Control Systems
This paper investigates the problem of synthesizing sensor deception attackers against privacy in the context of supervisory control of discrete-event systems (DES). We consider a DES plant controlled by a supervisor, which is subject to sensor deception attacks. Specifically, we consider an active attacker that can tamper with the observations received by the supervisor by, e.g., hacking on the communication channel between the sensors and the supervisor. The privacy requirement of the supervisory control system is to maintain initial-state opacity, i.e., it does not want to reveal the fact that it was initiated from a secret state during its operation. On the other hand, the attacker aims to deceive the supervisor, by tampering with its observations, such that initial-state opacity is violated due to incorrect control actions. In this work, we investigate from the attacker's point of view by presenting an effective approach for synthesizing sensor attack strategies threatening the privacy of the system. To this end, we propose the All Attack Structure (AAS) that records state estimates for both the supervisor and the attacker. This structure serves as a basis for synthesizing a sensor attack strategy. We also discuss how to simplify the synthesis complexity by leveraging the structural property of the initial-state privacy requirement. A running academic example is provided to illustrate the synthesis procedure.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Extended Version of "Distributed Adaptive Resilient Consensus Control for Uncertain Nonlinear Multiagent Systems Against Deception Attacks"
This paper studies distributed resilient consensus problem for a class of uncertain nonlinear multiagent systems susceptible to deception attacks. The attacks invade both sensor and actuator channels of each agent. A spe…
Effective faking of verbal deception detection with target-aligned adversarial attacks
Background: Deception detection through analysing language is a promising avenue using both human judgments and automated machine learning judgments. For both forms of credibility assessment, automated adversarial attack…
Adversarial AttackDeception DetectionLanguage ModelingLanguage Modelling+1VelLMes: A high-interaction AI-based deception framework
There are very few SotA deception systems based on Large Language Models. The existing ones are limited only to simulating one type of service, mainly SSH shells. These systems - but also the deception technologies not b…
Robust Deep Learning Ensemble against Deception
Deep neural network (DNN) models are known to be vulnerable to maliciously crafted adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the training data. How to protect a machine learn…
Adversarial RobustnessDeep LearningDenoisingDiversity+1Privacy-Aware Identity Cloning Detection based on Deep Forest
We propose a novel method to detect identity cloning of social-sensor cloud service providers to prevent the detrimental outcomes caused by identity deception. This approach leverages non-privacy-sensitive user profile d…
Deception Detection