Embedded sensors, computation, and communication have enabled the development of sophisticated control devices for a wide range of cyber-physical applications that include safety monitoring, surveillance, healthcare, motion planning, search and rescue, traffic monitoring, and power systems. However, the deployment of such devices has been slowed down by concerns regarding their sensitivity to modeling accuracy and their vulnerability to both stochastic failures and malicious attacks. Nowadays the efficiency will be defined by our potentials to adapt (complete autonomy) in decentralized, unknown and complex environments to enable capabilities beyond human limits. Until the achievement of such autonomy, cyber-physical technologies remain a critical issue. In the first part of the talk, I will combine methods from network security and control theory to design a new paradigm of proactive defense control mechanisms. For such a problem, we will define different modes of operation for the system that will let us isolate and identify suspicious actuators and sensors. Following the principles of moving target defense, we seek to maximize the unpredictability, quantified by the information entropy, in order to dynamically and stochastically switch the attack surface while optimally controlling the system. To better understand the behavior of the attackers that act on this system, a framework of bounded reasoning will be introduced to approximate the strategies utilized by attackers of different levels of intelligence. In the second part of the talk, I will present a novel model-free deep Q-learning control framework that will converge online, in real time to game-theoretic control solutions in the presence of persistent adversaries while guaranteeing closed-loop stability of the equilibrium point. The proposed approaches combine networked feedback control, game theory, network security, reinforcement learning, and serve as a tool for approaching difficult problems that without learning-based approaches are hard or impossible to solve.
Kyriakos G. Vamvoudakis was born in Athens, Greece. He received the Diploma (a 5 year degree, equivalent to a Master of Science) in Electronic and Computer Engineering from Technical University of Crete, Greece in 2006 with highest honors. After moving to the United States of America, he studied at The University of Texas and he received his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011 respectively. During the period from 2012 to 2016 he was a project research scientist at the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was an assistant professor at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech until 2018. He is now an assistant professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. His research interests include optimal control, reinforcement learning, and game theory. Recently, his research has focused on cyber-physical security, and safe autonomy. Prof. Vamvoudakis is the recipient of a 2018 National Science Foundation CAREER Award, the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011. He is a coauthor of one patent, more than 110 technical publications, and two books. He is the Program Chair, of the 8th International Conference on the Internet of Things (IoT 2018). He currently is an associate editor of the IEEE Computational Intelligent Magazine, an associate editor of Journal of Optimization Theory and Applications, an associate editor of Control Theory and Technology, a registered electrical/computer engineer (PE) and a member of the Technical Chamber of Greece. He is a Senior Member of IEEE.