Sachin Shetty – författare

AN ESSENTIAL GUIDE TO USING BLOCKCHAIN TO PROVIDE FLEXIBILITY, COST-SAVINGS, AND SECURITY TO DATA MANAGEMENT, DATA ANALYSIS, AND INFORMATION SHARINGBlockchain for Distributed Systems Security contains a description of the properties that underpin the formal foundations of Blockchain technologies and explores the practical issues for deployment in cloud and Internet of Things (IoT) platforms. The authors—noted experts in the field—present security and privacy issues that must be addressed for Blockchain technologies to be adopted for civilian and military domains. The book covers a range of topics including data provenance in cloud storage, secure IoT models, auditing architecture, and empirical validation of permissioned Blockchain platforms.The book's security and privacy analysis helps with an understanding of the basics of Blockchain and it explores the quantifying impact of the new attack surfaces introduced by Blockchain technologies and platforms. In addition, the book contains relevant and current updates on the topic. This important resource: Provides an overview of Blockchain-based secure data management and storage for cloud and IoTCovers cutting-edge research findings on topics including invariant-based supply chain protection, information sharing framework, and trust worthy information federationAddresses security and privacy concerns in Blockchain in key areas, such as preventing digital currency miners from launching attacks against mining pools, empirical analysis of the attack surface of Blockchain, and moreWritten for researchers and experts in computer science and engineering, Blockchain for Distributed Systems Security contains the most recent information and academic research to provide an understanding of the application of Blockchain technology.

An essential guide to the modeling and design techniques for securing systems that utilize the Internet of ThingsModeling and Design of Secure Internet of Things offers a guide to the underlying foundations of modeling secure Internet of Things' (IoT) techniques. The contributors—noted experts on the topic—also include information on practical design issues that are relevant for application in the commercial and military domains. They also present several attack surfaces in IoT and secure solutions that need to be developed to reach their full potential.The book offers material on security analysis to help with in understanding and quantifying the impact of the new attack surfaces introduced by IoT deployments. The authors explore a wide range of themes including: modeling techniques to secure IoT, game theoretic models, cyber deception models, moving target defense models, adversarial machine learning models in military and commercial domains, and empirical validation of IoT platforms. This important book: Presents information on game-theory analysis of cyber deceptionIncludes cutting-edge research finding such as IoT in the battlefield, advanced persistent threats, and intelligent and rapid honeynet generationContains contributions from an international panel of expertsAddresses design issues in developing secure IoT including secure SDN-based network orchestration, networked device identity management, multi-domain battlefield settings, and smart citiesWritten for researchers and experts in computer science and engineering, Modeling and Design of Secure Internet of Things contains expert contributions to provide the most recent modeling and design techniques for securing systems that utilize Internet of Things.

A comprehensive and up-to-date application of reinforcement learning concepts to offensive and defensive cybersecurity In Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing, a team of distinguished researchers delivers an incisive and practical discussion of reinforcement learning (RL) in cybersecurity that combines intelligence preparation for battle (IPB) concepts with multi-agent techniques. The authors explain how to conduct path analyses within networks, how to use sensor placement to increase the visibility of adversarial tactics and increase cyber defender efficacy, and how to improve your organization’s cyber posture with RL and illuminate the most probable adversarial attack paths in your networks. Containing entirely original research, this book outlines findings and real-world scenarios that have been modeled and tested against custom generated networks, simulated networks, and data. You’ll also find: A thorough introduction to modeling actions within post-exploitation cybersecurity events, including Markov Decision Processes employing warm-up phases and penalty scalingComprehensive explorations of penetration testing automation, including how RL is trained and tested over a standard attack graph constructPractical discussions of both red and blue team objectives in their efforts to exploit and defend networks, respectivelyComplete treatment of how reinforcement learning can be applied to real-world cybersecurity operational scenariosPerfect for practitioners working in cybersecurity, including cyber defenders and planners, network administrators, and information security professionals, Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing will also benefit computer science researchers.

This comprehensive book bridges the gap between cutting-edge artificial intelligence (AI) technology and its practical applications in dentistry and medicine. It empowers healthcare professionals, educators, and students to stay ahead in a rapidly evolving field by providing in-depth knowledge and practical insights into how AI is transforming diagnostics, treatment planning, patient care, and education. The book thoroughly explores AI-driven innovations, such as enhanced dental diagnostics, robotics in surgery, personalized medicine, and the integration of AI into medical and dental education. Packed with real-world examples and case studies, it offers actionable strategies for implementing AI tools and technologies in clinical practice. Readers will gain a deep understanding of the ethical considerations and legal frameworks essential for the responsible adoption of AI, ensuring they are well-equipped to navigate the complexities of modern healthcare.  Artificial Intelligence in Medicine and Dentistry: Advancing Science, Education, and Clinical Practice is designed to help professionals elevate their practice, improve patient outcomes, and contribute to the advancement of healthcare. It serves as a vital resource for anyone looking to harness the power of AI to revolutionize their field, fostering innovation and driving progress in dentistry and medicine.

This SpringerBrief presents adaptive resource allocation schemes for secondary users for dynamic spectrum access (DSA) in cognitive radio networks (CRNs) by considering Quality-of-Service requirements, admission control, power/rate control, interference constraints, and the impact of spectrum sensing or primary user interruptions. It presents the challenges, motivations, and applications of the different schemes.The authors discuss cloud-assisted geolocation-aware adaptive resource allocation in CRNs by outsourcing computationally intensive processing to the cloud. Game theoretic approaches are presented to solve resource allocation problems in CRNs. Numerical results are presented to evaluate the performance of the proposed methods. Adaptive Resource Allocation in Cognitive Radio Networks is designed for professionals and researchers working in the area of wireless networks. Advanced-level students in electrical engineering and computer science, especially those focused on wireless networks, will find this information helpful.

This book provides an overview of Moving Target Defense (MTD) and the importance of developing novel MTD schemes to protect distributed systems. It presents MTD-based research efforts to protect cloud data centers, along with network and security risk-aware approaches to place Virtual Machines (VM) in cloud data centers. These approaches include MTD-based network diversity models that enable an evaluation of the robustness of cloud data centers against potential zero-day attacks. Since these models can be used as a security metric the authors include different network configurations and policies, consider the similarity and dissimilarity of network resources, and account for minimum impact to maximum impact attacks. Also offered is a framework for determining the cost of MTD-based VM migration on cloud data centers. Designed for researchers and practitioners, Moving Target Defense for Distributed Systems enables readers to understand the potential of MTD capabilities. It enables defenders to change system or network behaviors, policies, and configurations automatically to keep potential attack surfaces protected. Advanced level students in computer science, especially those interested in networks and security, will benefit from this book.