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Aris Kanellopoulos
(Advisor: Kyriakos G. Vamvoudakis)
will defend a doctoral thesis entitled,
Control and Game-Theoretic Methods for Secure
Cyber-Physical-Human Systems
On
Tuesday, November 30 at 1:30 p.m.
Montgomery Knight 317
Bluejeans: https://bluejeans.com/3082303906/
Abstract
This work focuses on systems consisting of tightly interconnected physical and digital components. Those, aptly named, cyber-physical systems will be the core of the Fourth Industrial Revolution. Thus, cyber-physical systems will be called upon to interact with humans, either in a cooperative fashion, or as adversaries to malicious human agents that will seek to corrupt their operation. In this work, we will present methods that enable an autonomous system to operate safely among human agents and to gain an advantage in cyber-physical security scenarios by employing tools from control, game and learning theories. Our work revolves around three main axes: unpredictability-based defense, operation among agents with bounded rationality and safety for autonomous systems. In taking advantage of the complex nature of cyber-physical systems, our unpredictability-based defense work will focus on attacks on the physical components of such systems via a novel switching-based Moving Target Defense framework. Subsequently, we will take a more abstract view of complex system security by exploring the principles of bounded rationality. We will show how attackers of bounded rationality can coordinate in inducing erroneous decisions to a system while they remain stealthy. The principles of bounded rationality will be brought to control systems via the use of policy iteration algorithms, enabling data-driven attack prediction. The issue of intelligence in multi-agent scenarios will be further investigated through concepts of learning manipulation via a proposed framework where bounded rationality is understood as a hierarchy in learning, rather than optimizing, capability. This viewpoint will allow us to propose methods of exploiting the learning process of an opponent in order to affect their cognitive state with tools from optimal control theory. In the context of safety, we will explore verification and compositionality properties of linear systems that are designed to be added to a cascade network of similar systems. Finally, we will propose a framework that employs a hierarchical solution of temporal logic specifications and reinforcement learning problems for optimal tracking.
Committee
