@inproceedings{cb14c74b05d249c5a2132f57874d5697,
title = "Correction-Based Diffusion LMS Algorithms for Secure Distributed Estimation Under Attacks",
abstract = "In this paper, we mainly study the distributed estimation problem under attacks, which is mainly used to estimate the position parameters. To solve this problem, a correction-based secure diffusion least mean square (CS-dLMS) algorithm, which is a hybrid algorithm that composes of a non-cooperative LMS (nc-LMS) algorithm and a correction-based diffusion least-mean squares (C-dLMS) algorithm, is proposed for distributed estimation. The nc-LMS algorithm is used to provide a reliable reference system, which can detect reliable neighbor nodes by setting a threshold under network attacks. The correction-based least mean square algorithm can estimate unknown parameters by interacting with neighbor nodes. In order to guarantee the mean performance of the CS-dLMS algorithm under attracks, a sufficient condition is proposed. Finally, Simulation results are provided to verify the effectiveness of the proposed algorithm and it outperforms the C-dLMS algorithm and nc-LMS algorithm.",
keywords = "CS-dLMS algorithm, Communication attacks, Distributed estimation, Sensor attacks",
author = "Huining Chang and Wenling Li and Junping Du",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2019 ; Conference date: 26-10-2019 Through 27-10-2019",
year = "2020",
doi = "10.1007/978-981-32-9682-4\_43",
language = "英语",
isbn = "9789813296817",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "413--421",
editor = "Yingmin Jia and Junping Du and Weicun Zhang",
booktitle = "Proceedings of 2019 Chinese Intelligent Systems Conference - Volume I",
address = "德国",
}