Alotibi, Faris
(2023)
Physics and AI-Driven Anomaly Detection in Cyber-Physical Systems.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Organizations across various sectors are moving rapidly to digitization. Various applications in cyber-physical systems (CPSs) emerged from interconnectivity such as smart cities, autonomous vehicles, smart grids, and smart homes, utilizing advanced capabilities of the Internet of Things (IoTs), cloud computing, and machine learning. Interconnectivity also becomes a critical component in industrial systems such as smart manufacturing and production, smart oil and gas distribution grid, smart electric power grid, etc. These critical infrastructures and systems rely their operations on industrial IoT and learning-enabled components to handle the uncertainty and variability of the environment and increase the level of autonomy in making effective operational decisions. The prosperity and benefits of systems interconnectivity demand the fulfillment of functional requirements such as interoperability of communication and technology, efficiency and reliability, and real-time communication. Systems need to integrate with various communication technologies and standards, process and analyze shared data efficiently, ensure the integrity and accuracy of exchanged data, and execute their processes with tolerable delay. This creates enormous attack vectors targeting both physical and cyber components. Protection of systems interconnection and validation of communicated data against cyber and physical attacks become highly critical due to the massive consequences of
disruption or malfunction the attacks pose to critical systems.
In this dissertation, we tackle one of the prominent attacks in the CPS space, namely the false data injection attack (FDIA). FDIA is an attack executed to maliciously influence decisions, that is CPSs operational decisions such as opening a valve, changing wind turbine configurations,
charging/discharging energy storage system batteries, or coordinating autonomous vehicles driving. We focus on the development of anomaly detection techniques to protect CPSs from this emerging threat. The anomaly detection mechanisms leverage both physics of CPSs and AI to improve their detection capability as well as the CPSs' ability to mitigate the impact of FDIA on their operations.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
18 September 2023 |
| Date Type: |
Publication |
| Defense Date: |
31 July 2023 |
| Approval Date: |
18 September 2023 |
| Submission Date: |
8 August 2023 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
174 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Computing and Information > Information Science |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Anomaly Detection; Cyber Physical Systems; Cyber Attacks; Artificial Intelligence; Machine Learning; Insider Threat; False Data Injection Attacks; Information Security; Attack Detection; Attack Mitigation; |
| Date Deposited: |
18 Sep 2023 14:16 |
| Last Modified: |
18 Sep 2023 14:16 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/45336 |
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