Data-driven detection and verification of replay attacks on industrial control systems
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Gargoum, Sara
issued: 2024-07-13T07:00:00Z
issued: 2023
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: With the advent of Industrial Internet of Things and the push towards digitization, industrial control systems have undergone an increase in connectivity, which enables remote monitoring and control. This in turn has led to their increased vulnerability to several cyberattacks. These attacks can have severe consequences, including equipment damage, service disruption, and even compromising public safety. This thesis focuses on a certain type of cyberattacks, called replay attack. A replay attack involves capturing legitimate communication signals between components in an industrial control system and replaying it later to disrupt system operations or perform other attacks. This thesis presents a comprehensive study on the detection of replay attacks on sensor measurements in industrial control systems. Utilising the vast amount of operational data (i.e. sensor measurements) generated by industrial control systems, a novel data-driven two-stage detection and verification framework is proposed. The proposed framework combines statistical techniques, signal processing and deep learning to detect replay attacks on industrial control systems. The first stage of the detection method consists of continuous monitoring of sensor measurements by performing change-point detection based on the corresponding matrix profile. This stage provides an early indicator of a potential replay attack. The second stage provides a validation of whether the detected change-point is due to a replay attack or not. This stage consists of performing time-frequency analysis using short-time Fourier transform to generate a spectrogram, introducing spectral features to the time-series sensor measurements. Then, the spectrogram is split into image frames, creating spectro-temporal features. A Convolutional Long- Short Term Memory based autoencoder (ConvLSTM-AE) is designed to capture these spectro-temporal features in an unsupervised manner, where a replay attack is detected based on the reconstruct…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85543
note: Data-Driven Detection andVerification of Replay Attacks onIndustrial Control SystemsbySara GargoumB.Sc. Hons., United Arab Emirates University, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)August 2023© Sara Gargoum, 2023The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis entitled:Data-Driven Detection and Verification of Replay Attackson Industrial Control Systemssubmitted by Sara Gargoum in partial fulfilment of the requirements of thedegree of Master of Applied ScienceAhmad Al-Dabbagh, School of EngineeringSupervisorChen Feng, School of EngineeringCo-SupervisorAnas Chaaban, School of EngineeringSupervisory Committee MemberZheng Liu, School of EngineeringSupervisory Committee MemberRyozo Nagamune, University of British Columbia - VancouverUniversity ExamineriiAbstractWith the advent of Industrial Internet of Things and the push towardsdigitization, industrial control systems have undergone an increase in con-nectivity, which enables remote monitoring and control. This in turn has ledto their increased vulnerability to several cyberattacks. These attacks canhave severe consequences, including equipment damage, service disruption,and even compromising public safety. This thesis focuses on a certain typeof cyberattacks, called replay attack. A replay attack involves capturinglegitimate communication signals between components in an industrial con-trol system and replaying it later to disrupt system operations or performother attacks.This thesis presents a comprehensive study on the detection of replayattacks on sensor measurements in industrial control systems. Utilising thevast amount of operational data (i.e. sensor measurements) generated byindustrial control systems, a novel data-driven two-stage detection and verifi-cation frame…
has type: Thesis/Dissertation
date issued: 2023-09
is shown at: 10.14288/1.0435495
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Al-Dabbagh, Ahmad
contributor: Feng, Chen
type: Text
identifier URI: http://hdl.handle.net/2429/85543