Thesis Defence: Data-Driven Mitigation of False Data Injection Cyberattacks in Networked Control Systems
November 19 at 3:00 pm - 7:00 pm
Mohammadamin Lari, supervised by Dr. Ahmad Al-Dabbagh, will defend their thesis titled “Data-Driven Mitigation of False Data Injection Cyberattacks in Networked Control Systems” in partial fulfillment of the requirements for the degree of Master of Applied Science in Electrical Engineering.
An abstract for Mohammadamin Lari’s thesis is included below.
Defences are open to all members of the campus community as well as the general public. Please email ahmad.aldabbagh@ubc.ca to receive the Zoom link for this defence.
ABSTRACT
The rapid advancement of digital technologies has resulted in an increase in data generation and availability, along with breakthroughs in artificial intelligence, machine learning, and access to high-performance computing resources. These developments have drawn attention into data-driven approaches to tackle various challenges in industry. One of the most important challenges is enhancing security and protecting systems against malicious cyberattacks to prevent their catastrophic consequences. This thesis focuses on one of most common types of cyberattacks, called false data injection attack, which compromises the integrity of data transmitted over communication networks.
This thesis aims to enhance the resiliency of networked control systems and ensure their safe operation in the presence of malicious activities through mitigating the impacts of false data injection attacks in real-time using a novel two-stage data-driven framework. The proposed framework utilizes a stacked ensemble learning architecture, including a variety of time series forecasting models, and a model selection module to preserve the computational efficiency of the developed framework. The first stage of the proposed framework involves meta learning to select a time series forecasting model from the stack for mitigation purposes. In the second stage, the selected model mitigates false data injection attacks in real-time. The proposed method’s effectiveness is demonstrated through rigorous simulations involving the formation control of differential-drive mobile robots.
