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This book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.
Book Synopsis Adversary-Aware Learning Techniques and Trends in Cybersecurity by : Prithviraj Dasgupta
Download or read book Adversary-Aware Learning Techniques and Trends in Cybersecurity written by Prithviraj Dasgupta and published by Springer Nature. This book was released on 2021-01-22 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.
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.
Book Synopsis Adversarial Machine Learning by : Aneesh Sreevallabh Chivukula
Download or read book Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and published by Springer Nature. This book was released on 2023-03-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
This book constitutes the proceedings of the Workshops held in conjunction with SAFECOMP 2023, held in Toulouse, France, during September 19, 2023. The 35 full papers included in this volume were carefully reviewed and selected from 49 submissions. - - 8th International Workshop on Assurance Cases for Software-intensive Systems (ASSURE 2023) - - 18th International Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems (DECSoS 2023) - - 10th International Workshop on Next Generation of System Assurance Approaches for Critical Systems (SASSUR 2023) - - Second International Workshop on Security and Safety Interactions (SENSEI 2023) - - First International Workshop on Safety/ Reliability/ Trustworthiness of Intelligent Transportation Systems (SRToITS 2023) - - 6th International Workshop on Artificial Intelligence Safety Engineering (WAISE 2023)
Book Synopsis Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops by : Jérémie Guiochet
Download or read book Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops written by Jérémie Guiochet and published by Springer Nature. This book was released on 2023-10-15 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Workshops held in conjunction with SAFECOMP 2023, held in Toulouse, France, during September 19, 2023. The 35 full papers included in this volume were carefully reviewed and selected from 49 submissions. - - 8th International Workshop on Assurance Cases for Software-intensive Systems (ASSURE 2023) - - 18th International Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems (DECSoS 2023) - - 10th International Workshop on Next Generation of System Assurance Approaches for Critical Systems (SASSUR 2023) - - Second International Workshop on Security and Safety Interactions (SENSEI 2023) - - First International Workshop on Safety/ Reliability/ Trustworthiness of Intelligent Transportation Systems (SRToITS 2023) - - 6th International Workshop on Artificial Intelligence Safety Engineering (WAISE 2023)
This book constitutes the refereed proceedings of the 15th International Conference on Augmented Cognition, AC 2021, held as part of the 23rd International Conference, HCI International 2021, held as a virtual event, in July 2021. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. AC 2021 includes a total of 32 papers; they were organized in topical sections named: BCI and brain activity measurement physiological measuring and human performance; modelling human cognition; and augmented cognition in complex environments.
Book Synopsis Augmented Cognition by : Dylan D. Schmorrow
Download or read book Augmented Cognition written by Dylan D. Schmorrow and published by Springer Nature. This book was released on 2021-07-03 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 15th International Conference on Augmented Cognition, AC 2021, held as part of the 23rd International Conference, HCI International 2021, held as a virtual event, in July 2021. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. AC 2021 includes a total of 32 papers; they were organized in topical sections named: BCI and brain activity measurement physiological measuring and human performance; modelling human cognition; and augmented cognition in complex environments.
Book Synopsis Network Security Empowered by Artificial Intelligence by : Yingying Chen
Download or read book Network Security Empowered by Artificial Intelligence written by Yingying Chen and published by Springer Nature. This book was released on with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Today’s cyber defenses are largely static allowing adversaries to pre-plan their attacks. In response to this situation, researchers have started to investigate various methods that make networked information systems less homogeneous and less predictable by engineering systems that have homogeneous functionalities but randomized manifestations. The 10 papers included in this State-of-the Art Survey present recent advances made by a large team of researchers working on the same US Department of Defense Multidisciplinary University Research Initiative (MURI) project during 2013-2019. This project has developed a new class of technologies called Adaptive Cyber Defense (ACD) by building on two active but heretofore separate research areas: Adaptation Techniques (AT) and Adversarial Reasoning (AR). AT methods introduce diversity and uncertainty into networks, applications, and hosts. AR combines machine learning, behavioral science, operations research, control theory, and game theory to address the goal of computing effective strategies in dynamic, adversarial environments.
Book Synopsis Adversarial and Uncertain Reasoning for Adaptive Cyber Defense by : Sushil Jajodia
Download or read book Adversarial and Uncertain Reasoning for Adaptive Cyber Defense written by Sushil Jajodia and published by Springer Nature. This book was released on 2019-08-30 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today’s cyber defenses are largely static allowing adversaries to pre-plan their attacks. In response to this situation, researchers have started to investigate various methods that make networked information systems less homogeneous and less predictable by engineering systems that have homogeneous functionalities but randomized manifestations. The 10 papers included in this State-of-the Art Survey present recent advances made by a large team of researchers working on the same US Department of Defense Multidisciplinary University Research Initiative (MURI) project during 2013-2019. This project has developed a new class of technologies called Adaptive Cyber Defense (ACD) by building on two active but heretofore separate research areas: Adaptation Techniques (AT) and Adversarial Reasoning (AR). AT methods introduce diversity and uncertainty into networks, applications, and hosts. AR combines machine learning, behavioral science, operations research, control theory, and game theory to address the goal of computing effective strategies in dynamic, adversarial environments.
Machine Learning has shown great promise when combined with large volumes of historical data and produces great results when combined with contextual properties. In the world of the Internet of Things, the extraction of information regarding context, or contextual information, is increasingly prominent with scientific advances. Combining such advancements with artificial intelligence is one of the themes in this thesis. Particularly, there are two major areas of interest: context-aware attacker modelling and context-aware defensive methods. Both areas use authentication methods to either infiltrate or protect digital systems. After a brief introduction in chapter 1, chapter 2 discusses the current extracted contextual information within cybersecurity studies, and how machine learning accomplishes a variety of cybersecurity goals. Chapter 3 introduces an attacker injection model, championing the adversarial methods. Then, chapter 4 extracts contextual data and provides an intelligent machine learning technique to mitigate anomalous behaviours. Chapter 5 explores the feasibility of adopting a similar defensive methodology in the cyber-physical domain, and future directions are presented in chapter 6. Particularly, we begin this thesis by explaining the need for further improvements in cybersecurity using contextual information and discuss its feasibility, now that ubiquitous sensors exist in our everyday lives. These sensors often show a high correlation with user identity in surprising combinations. Our first contribution lay within the domain of Mobile CrowdSensing (MCS). Despite its benefits, MCS requires proper security solutions to prevent various attacks, notably injection attacks. Our smart-injection model, SINAM, monitors data traffic in an online-learning manner, simulating an injection model with undetection rates of 99%. SINAM leverages contextual similarities within a given sensing campaign to mimic anomalous injections. On the flip-side, we investigate how contextual features can be utilized to improve authentication methods in an enterprise context. Also motivated by the emergence of omnipresent mobile devices, we expand the Spatio-temporal features of unfolding contexts by introducing three contextual metrics: document shareability, document valuation, and user cooperation. These metrics are vetted against modern machine learning techniques and achieved an average of 87% successful authentication attempts. Our third contribution aims to further improve such results but introducing a Smart Enterprise Access Control (SEAC) technique. Combining the new contextual metrics with SEAC achieved an authenticity precision of 99% and a recall of 97%. Finally, the last contribution is an introductory study on risk analysis and mitigation using context. Here, cyber-physical coupling metrics are created to extract a precise representation of unfolding contexts in the medical field. The presented consensus algorithm achieves initial system conveniences and security ratings of 88% and 97% with these news metrics. Even as a feasibility study, physical context extraction shows good promise in improving cybersecurity decisions. In short, machine learning is a powerful tool when coupled with contextual data and is applicable across many industries. Our contributions show how the engineering of contextual features, adversarial and defensive methods can produce applicable solutions in cybersecurity, despite minor shortcomings.
Book Synopsis Context-Awareness for Adversarial and Defensive Machine Learning Methods in Cybersecurity by : Kyle Quintal
Download or read book Context-Awareness for Adversarial and Defensive Machine Learning Methods in Cybersecurity written by Kyle Quintal and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning has shown great promise when combined with large volumes of historical data and produces great results when combined with contextual properties. In the world of the Internet of Things, the extraction of information regarding context, or contextual information, is increasingly prominent with scientific advances. Combining such advancements with artificial intelligence is one of the themes in this thesis. Particularly, there are two major areas of interest: context-aware attacker modelling and context-aware defensive methods. Both areas use authentication methods to either infiltrate or protect digital systems. After a brief introduction in chapter 1, chapter 2 discusses the current extracted contextual information within cybersecurity studies, and how machine learning accomplishes a variety of cybersecurity goals. Chapter 3 introduces an attacker injection model, championing the adversarial methods. Then, chapter 4 extracts contextual data and provides an intelligent machine learning technique to mitigate anomalous behaviours. Chapter 5 explores the feasibility of adopting a similar defensive methodology in the cyber-physical domain, and future directions are presented in chapter 6. Particularly, we begin this thesis by explaining the need for further improvements in cybersecurity using contextual information and discuss its feasibility, now that ubiquitous sensors exist in our everyday lives. These sensors often show a high correlation with user identity in surprising combinations. Our first contribution lay within the domain of Mobile CrowdSensing (MCS). Despite its benefits, MCS requires proper security solutions to prevent various attacks, notably injection attacks. Our smart-injection model, SINAM, monitors data traffic in an online-learning manner, simulating an injection model with undetection rates of 99%. SINAM leverages contextual similarities within a given sensing campaign to mimic anomalous injections. On the flip-side, we investigate how contextual features can be utilized to improve authentication methods in an enterprise context. Also motivated by the emergence of omnipresent mobile devices, we expand the Spatio-temporal features of unfolding contexts by introducing three contextual metrics: document shareability, document valuation, and user cooperation. These metrics are vetted against modern machine learning techniques and achieved an average of 87% successful authentication attempts. Our third contribution aims to further improve such results but introducing a Smart Enterprise Access Control (SEAC) technique. Combining the new contextual metrics with SEAC achieved an authenticity precision of 99% and a recall of 97%. Finally, the last contribution is an introductory study on risk analysis and mitigation using context. Here, cyber-physical coupling metrics are created to extract a precise representation of unfolding contexts in the medical field. The presented consensus algorithm achieves initial system conveniences and security ratings of 88% and 97% with these news metrics. Even as a feasibility study, physical context extraction shows good promise in improving cybersecurity decisions. In short, machine learning is a powerful tool when coupled with contextual data and is applicable across many industries. Our contributions show how the engineering of contextual features, adversarial and defensive methods can produce applicable solutions in cybersecurity, despite minor shortcomings.
Intelligent Approach to Cyber Security provides details on the important cyber security threats and its mitigation and the influence of Machine Learning, Deep Learning and Blockchain technologies in the realm of cyber security. Features: Role of Deep Learning and Machine Learning in the Field of Cyber Security Using ML to defend against cyber-attacks Using DL to defend against cyber-attacks Using blockchain to defend against cyber-attacks This reference text will be useful for students and researchers interested and working in future cyber security issues in the light of emerging technology in the cyber world.
Book Synopsis Intelligent Approaches to Cyber Security by : Narendra M Shekokar
Download or read book Intelligent Approaches to Cyber Security written by Narendra M Shekokar and published by CRC Press. This book was released on 2023-10-11 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Approach to Cyber Security provides details on the important cyber security threats and its mitigation and the influence of Machine Learning, Deep Learning and Blockchain technologies in the realm of cyber security. Features: Role of Deep Learning and Machine Learning in the Field of Cyber Security Using ML to defend against cyber-attacks Using DL to defend against cyber-attacks Using blockchain to defend against cyber-attacks This reference text will be useful for students and researchers interested and working in future cyber security issues in the light of emerging technology in the cyber world.
Focuses on learning vulnerabilities and cyber security. The book gives detail on the new threats and mitigation methods in the cyber security domain, and provides information on the new threats in new technologies such as vulnerabilities in deep learning, data privacy problems with GDPR, and new solutions.
Book Synopsis Cyber Security and Adversarial Machine Learning by : Ferhat Ozgur Catak
Download or read book Cyber Security and Adversarial Machine Learning written by Ferhat Ozgur Catak and published by . This book was released on 2021-10-30 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on learning vulnerabilities and cyber security. The book gives detail on the new threats and mitigation methods in the cyber security domain, and provides information on the new threats in new technologies such as vulnerabilities in deep learning, data privacy problems with GDPR, and new solutions.
This textbook presents a proven, mature Model-Based Systems Engineering (MBSE) methodology that has delivered success in a wide range of system and enterprise programs. The authors introduce MBSE as the state of the practice in the vital Systems Engineering discipline that manages complexity and integrates technologies and design approaches to achieve effective, affordable, and balanced system solutions to the needs of a customer organization and its personnel. The book begins with a summary of the background and nature of MBSE. It summarizes the theory behind Object-Oriented Design applied to complex system architectures. It then walks through the phases of the MBSE methodology, using system examples to illustrate key points. Subsequent chapters broaden the application of MBSE in Service-Oriented Architectures (SOA), real-time systems, cybersecurity, networked enterprises, system simulations, and prototyping. The vital subject of system and architecture governance completes the discussion. The book features exercises at the end of each chapter intended to help readers/students focus on key points, as well as extensive appendices that furnish additional detail in particular areas. The self-contained text is ideal for students in a range of courses in systems architecture and MBSE as well as for practitioners seeking a highly practical presentation of MBSE principles and techniques.
Book Synopsis Effective Model-Based Systems Engineering by : John M. Borky
Download or read book Effective Model-Based Systems Engineering written by John M. Borky and published by Springer. This book was released on 2018-09-08 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents a proven, mature Model-Based Systems Engineering (MBSE) methodology that has delivered success in a wide range of system and enterprise programs. The authors introduce MBSE as the state of the practice in the vital Systems Engineering discipline that manages complexity and integrates technologies and design approaches to achieve effective, affordable, and balanced system solutions to the needs of a customer organization and its personnel. The book begins with a summary of the background and nature of MBSE. It summarizes the theory behind Object-Oriented Design applied to complex system architectures. It then walks through the phases of the MBSE methodology, using system examples to illustrate key points. Subsequent chapters broaden the application of MBSE in Service-Oriented Architectures (SOA), real-time systems, cybersecurity, networked enterprises, system simulations, and prototyping. The vital subject of system and architecture governance completes the discussion. The book features exercises at the end of each chapter intended to help readers/students focus on key points, as well as extensive appendices that furnish additional detail in particular areas. The self-contained text is ideal for students in a range of courses in systems architecture and MBSE as well as for practitioners seeking a highly practical presentation of MBSE principles and techniques.
