MURAL - Maynooth University Research Archive Library



    SINR-Based DoS Attack on Remote State Estimation: A Game-Theoretic Approach


    Li, Yuzhe and Quevedo, Daniel E. and Dey, Subhrakanti and Shi, Ling (2017) SINR-Based DoS Attack on Remote State Estimation: A Game-Theoretic Approach. IEEE Transactions on Control of Network Systems, 4 (3). pp. 632-642. ISSN 2325-5870

    [img]
    Preview
    Download (889kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    We consider remote state estimation of cyberphysical systems under signal-to-interference-plus-noise ratio-based denial-of-service attacks. A sensor sends its local estimate to a remote estimator through a wireless network that may suffer interference from an attacker. Both the sensor and the attacker have energy constraints. We first study an associated two-player game when multiple power levels are available. Then, we build a Markov game framework to model the interactive decision-making process based on the current state and information collected from previous time steps. To solve the associated optimality (Bellman) equations, a modified Nash Q-learning algorithm is applied to obtain the optimal solutions. Numerical examples and simulations are provided to demonstrate our results.

    Item Type: Article
    Keywords: Cyberphysical systems; game theory; remote state estimation; security; wireless sensors;
    Academic Unit: Faculty of Arts,Celtic Studies and Philosophy > Language Centre
    Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 11460
    Identification Number: https://doi.org/10.1109/TCNS.2016.2549640
    Depositing User: Subhrakanti Dey
    Date Deposited: 24 Oct 2019 12:35
    Journal or Publication Title: IEEE Transactions on Control of Network Systems
    Publisher: IEEE
    Refereed: Yes
    URI:
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads