Control-theoretic Methods for Cyber-Physical Security presents novel results on security and defense methodologies applied to cyber-physical systems. The book adopts the viewpoint… Read more
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Control-theoretic Methods for Cyber-Physical Security presents novel results on security and defense methodologies applied to cyber-physical systems. The book adopts the viewpoint of control and game theories, modeling these autonomous platforms as dynamical systems and proposing algorithmic frameworks that both proactively and reactively shield the systems against catastrophic failures. Algorithms presented employ model-based and data-driven techniques to security, ranging from model-free detection mechanisms to unpredictability-based defense approaches. This book will be a reference to the research community in identifying approaches to security that go beyond robustification techniques and give attention to the tight interplay between the physical and digital devices of the system. Algorithms that can be readily used in a variety of application domains where systems are subject to different kinds of attacks are also included.
Serves as a bibliography on different aspects of security in cyber-physical systems
Presents technical results on our approaches to security that offer new perspectives on the topic by combining principles from a variety of disciplines
Provides a treatment of open problems in the field of security through the prism of the rigorous formulations of control and game theory
Researchers, engineers, and graduate students in the fields of control theory, IoT, cyber-physical systems, and machine learning
1. Introduction
1.1. Cyber-physical systems and security
1.1.1. The viewpoint of computer scientists
1.1.2. The viewpoint of control theorists
1.2. Background on control theory
2. Control-theoretic intrusion detection
2.1. Bellman-based detection
2.2. Statistics-based detection
2.3. Optimization-based detection
3. Redundancy-based defense
3.1. Redundancy-driven resilience and unpredictability
3.2. Moving Target Defense
3.3. Learning-based actuator and sensor placement for security
3.4. Defense against DoS attacks
4. Timing faults and attacks
4.1. Timing issues in CPS
4.2. Clock offsets during autonomous decision-making
5. Adversarial modelling
5.1. Game theory as a modelling tool
5.2. Bounded rationality in dynamical environments
5.3. Dynamic bounded rationality for deception
5.4. Non-equilibrium MDPs
6. Future research directions
No. of pages: 300
Language: English
Published: June 1, 2024
Imprint: Academic Press
Paperback ISBN: 9780443154089
eBook ISBN: 9780443154096
AK
Aris Kanellopoulos
Aris Kanellopoulos received his diploma equivalent to a Master of Science in Mechanical Engineering from the National Technical University of Athens, Greece in 2017. He studied at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech, and was awarded a Ph.D. in Aerospace Engineering at the Georgia Institute of Technology, working with Professor Kyriakos G. Vamvoudakis. He is currently a Research Engineer within Professor Vamvoudakis’ group.
Affiliations and expertise
Research Engineer, Georgia Institute of Technology, Atlanta, GA, USA
LZ
Lijing Zhai
Lijing Zhai received her M.S. in Power Engineering and Engineering Thermophysics from Beihang University, Beijing, China, in 2018. She is currently working toward the Ph.D. degree with Professor Kyriakos G. Vamvoudakis at the Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA. Her current research interests include cyber-physical security, optimal and learning-based control.
Affiliations and expertise
Ph.D. degree with Professor Kyriakos G. Vamvoudakis at the Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
FF
Filippos Fotiadis
Filippos Fotiadis received his diploma equivalent to a Master of Science in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece in 2018. He is currently pursuing his Ph.D. degree at The Daniel Guggenheim School of Aerospace Engineering at Georgia Institute of Technology.
Affiliations and expertise
The Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, USA
KV
Kyriakos Vamvoudakis
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 the 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 at Arlington with Frank L. Lewis as his advisor, and he received his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011 respectively. From May 2011 to January 2012, he was working as an Adjunct Professor and Faculty Research Associate at the University of Texas at Arlington and at the Automation and Robotics Research Institute. During the period from 2012 to 2016 he was 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 currently serves as the Dutton-Ducoffe Endowed Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He holds a secondary appointment in the School of Electrical and Computer Engineering. His expertise is in reinforcement learning, control theory, game theory, cyber-physical security, bounded rationality, and safe/assured autonomy. Dr. Vamvoudakis is the recipient of a 2019 ARO YIP award, a 2018 NSF CAREER award, a 2018 DoD Minerva Research Initiative Award, a 2021 GT Chapter Sigma Xi Young Faculty Award and his work has been recognized with best paper nominations and several international awards including 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.
Affiliations and expertise
Assistant Professor, Virginia Tech, Blacksburg, VA, USA