Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163817
Title: Cyber attack detection and isolation for a quadrotor UAV with modified sliding innovation sequences
Authors: Xiao, Jiaping
Feroskhan, Mir
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Xiao, J. & Feroskhan, M. (2022). Cyber attack detection and isolation for a quadrotor UAV with modified sliding innovation sequences. IEEE Transactions On Vehicular Technology, 71(7), 7202-7214. https://dx.doi.org/10.1109/TVT.2022.3170725
Project: RG69/20
Journal: IEEE Transactions on Vehicular Technology
Abstract: Common vulnerabilities in typical intelligent cyber-physical systems such as unmanned aerial vehicles (UAVs) can be easily exploited by cyber attackers to cause serious accidents and harm. For successful UAV operations, security against cyber attacks is imperative. In this paper, we propose a modified sliding innovation sequences (MSIS) detector, based on the extended Kalman filter optimal state estimation, for a dynamic quadrotor system to detect cyber attacks inflicted on both its actuators and sensors in real time. These cyber attacks include random attacks, false data injection (FDI) attacks and denial-of-service (DoS) attacks. The MSIS detector computes the operator norm of the normalized innovation (residual) sequence within a sliding time window and triggers the alarm if the value is above the preset threshold. For a quadrotor undergoing rapid turns in a complex trajectory, the detector observes a reduced false alarm rate as compared to other state estimation-based detectors. To address the initial estimation error problem, we implement an iteration procedure to initiate and calibrate the detector. By evaluating the sample covariance of the normalized innovation sequence, the MSIS detector has the capability to isolate cyber attacks. Finally, simulation results of a quadrotor in a periodic, complex trajectory flight are provided to verify the effectiveness of the MSIS detection and isolation method.
URI: https://hdl.handle.net/10356/163817
ISSN: 0018-9545
DOI: 10.1109/TVT.2022.3170725
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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