This vital work for researchers and graduate students focuses on resilience estimation and control of cyber-physical networked systems using attacker-defender game theory. It presents attack and defense strategies and describes the design and resilience of control systems to withstand cyberattacks. Complex control systems, including cyber-physical and cloud control systems, are in open network environments and are often confronted with threats from cyberspace, physical space and even cloud service. With diversified and intelligent attack patterns and improvements in attack capabilities, non-contact damage can be widespread. In this book, the authors use a formal, mathematical approach to introduce their recent research findings to describe and design attack and defense strategies using game theoretic method. The book is divided into three sections, focusing on strategies for resilience against deception attacks and DoS attacks, and protecting cloud control systems against threats. In these sections, the authors address topics such as secure and distributed filtering, attack detection and disturbance rejection, resilient state estimation, and resilient control, and techniques such as Stackelberg games, hierarchical games, and active eavesdropping. Through this book readers will be able to design effective defense strategies for complex control system to achieve resilience for closed-control cyber physical systems, network and cloud systems. This book is a vital resource for graduate students and academic researchers who are familiar with the concepts related to cyberattack and defense and who have a related research background. To maximize their benefit from this book, readers are recommended to have a strong mathematical foundation as the book takes a mathematical approach to the concepts and strategies described within.

This book adopts a systematic view of the control systems in cyber-physical systems including the security control of the optimal control system, security control of the non-cooperative game system, quantify the impact of the Denial-of-Service attacks on the optimal control system, and the adaptive security control of the networked control systems. Because the cyber-physical system is a hybrid system, it adopts cross layer approach to handle the security control of the CPS. It presents a number of attack models according to the attack scenario and defense facilities, and a number of cross-layer co-design methodologies to secure the control of CPS.

This book explores fundamental scientific problems essential for autonomous cyber defense. Specific areas include: Game and control theory-based moving target defenses (MTDs) and adaptive cyber defenses (ACDs) for fully autonomous cyber operations; The extent to which autonomous cyber systems can be designed and operated in a framework that is significantly different from the human-based systems we now operate; On-line learning algorithms, including deep recurrent networks and reinforcement learning, for the kinds of situation awareness and decisions that autonomous cyber systems will require; Human understanding and control of highly distributed autonomous cyber defenses; Quantitative performance metrics for the above so that autonomous cyber defensive agents can reason about the situation and appropriate responses as well as allowing humans to assess and improve the autonomous system. This book establishes scientific foundations for adaptive autonomous cyber systems and ultimately brings about a more secure and reliable Internet. The recent advances in adaptive cyber defense (ACD) have developed a range of new ACD techniques and methodologies for reasoning in an adaptive environment. Autonomy in physical and cyber systems promises to revolutionize cyber operations. The ability of autonomous systems to execute at scales, scopes, and tempos exceeding those of humans and human-controlled systems will introduce entirely new types of cyber defense strategies and tactics, especially in highly contested physical and cyber environments. The development and automation of cyber strategies that are responsive to autonomous adversaries pose basic new technical challenges for cyber-security. This book targets cyber-security professionals and researchers (industry, governments, and military). Advanced-level students in computer science and information systems will also find this book useful as a secondary textbook.

A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

Networked Control Systems: Cloud Control and Secure Control explores new technological developments in networked control systems (NCS), including new techniques, such as event-triggered, secure and cloud control. It provides the fundamentals and underlying issues of networked control systems under normal operating environments and under cyberphysical attack. The book includes a critical examination of the principles of cloud computing, cloud control systems design, the available techniques of secure control design to NCS's under cyberphysical attack, along with strategies for resilient and secure control of cyberphysical systems. Smart grid infrastructures are also discussed, providing diagnosis methods to analyze and counteract impacts. Finally, a series of practical case studies are provided to cover a range of NCS's. This book is an essential resource for professionals and graduate students working in the fields of networked control systems, signal processing and distributed estimation. - Provides coverage of cloud-based approaches to control systems and secure control methodologies to protect cyberphysical systems against various types of malicious attacks - Provides an overview of control research literature and explores future developments and solutions - Includes case studies that offer solutions for issues with modeling, quantization, packet dropout, time delay and communication constraints

This book constitutes the refereed proceedings of the Third International Conference on Decision and Game Theory for Security, GameSec 2012, held in Budapest, Hungary, in November 2012. The 18 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on secret communications, identification of attackers, multi-step attacks, network security, system defense, and applications security.

The three-volume set CCIS 1467, CCIS 1468, and CCIS 1469 constitutes the thoroughly refereed proceedings of the 7th International Conference on Life System Modeling and Simulation, LSMS 2021, and of the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, held in Hangzhou, China, in October 2021. The 159 revised papers presented were carefully reviewed and selected from over 430 submissions. The papers of this volume are organized in topical sections on: Medical Imaging and Analysis Using Intelligence Computing; Biomedical signal processing, imaging, visualization and surgical robotics; Computational method in taxonomy study and neural dynamics; Intelligent medical apparatus, clinical applications and intelligent design of biochips; Power and Energy Systems; Computational Intelligence in Utilization of Clean and Renewable Energy Resources, and Intelligent Modelling, Control and Supervision for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Control Methods in Energy Infrastructure Development and Distributed Power Generation Systems; Intelligent Modeling, Simulation and Control of Power Electronics and Power Networks; Intelligent Techniques for Sustainable Energy and Green Built Environment, Water Treatment and Waste Management; Intelligent Robot and Simulation; Intelligent Data Processing, Analysis and Control in Complex Systems; Advanced Neural Network Theory and Algorithms; Advanced Computational Methods and Applications; Fuzzy, Neural, and Fuzzy-neuro Hybrids; Intelligent Modelling, Monitoring, and Control of Complex Nonlinear Systems; Intelligent manufacturing, autonomous systems, intelligent robotic systems; Computational Intelligence and Applications.

Cyber Security for Industrial Control Systems: From the Viewpoint of Close-Loop provides a comprehensive technical guide on up-to-date new secure defending theories and technologies, novel design, and systematic understanding of secure architecture with practical applications. The book consists of 10 chapters, which are divided into three parts.The

This book constitutes the refereed proceedings of the 7th International Conference on Decision and Game Theory for Security, GameSec 2016, held in New York, NY, USA, in November 2016. The 18 revised full papers presented together with 8 short papers and 5 poster papers were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections on network security; security risks and investments; special track-validating models; decision making for privacy; security games; incentives and cybersecurity mechanisms; and intrusion detection and information limitations in security.