Diep N. Nguyen - Böcker

Advanced Machine Learning for Cyber-Attack Detection in IoT Networks analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, along with their applications in detecting and preventing cyberattacks in future IoT systems. Chapters investigate the key challenges and vulnerabilities found in IoT security, how to handle challenges in data collection and pre-processing specific to IoT environments, as well as what metrics to consider for evaluating the performance of machine learning models. Other sections look at the training, validation, and evaluation of supervised learning models and present case studies and examples that demonstrate the application of supervised learning in IoT security.Presents a comprehensive overview of research on IoT security threats and potential attacksInvestigates machine learning techniques, their mathematical foundations, and their application in cybersecurityPresents metrics for evaluating the performance of machine learning models as well as benchmark datasets and evaluation frameworks for assessing IoT systems

This book lays a systematic foundation for professionals, researchers, and industry readers who are interested the applications and implications of generative AI for cybersecurity. It covers the latest advances in generative AI and its applications, risks, and opportunities in cybersecurity.The authors first introduce the fundamental background of generative AI, the latest cybersecurity issues, and related potential applications in cybersecurity systems. Following this, they comprehensively review the state-of-the-art research and development, covering various aspects of generative AI applications in this area and related challenges and issues, such as training data availability, computational complexity, generalization to different scenarios, AI governance, quantum-empowered AI and many more. These discussions provide a strong understanding of recent advances in the two fields of generative AI and cybersecurity and the convergence of these domains, which will help readers to shape the field as it matures. Hands-on experiments presented throughout will also give them the practical skills for success. By leveraging its capabilities, readers can overcome challenges, understand the risks, enhance performance, and unlock new opportunities for handling the challenges of cybersecurity with generative AI. Consequently, they will be able to apply their knowledge to utilize generative AI in cybersecurity applications to prevent economic and other losses due to cyber risks such as phishing, fake news, deepfake-based fraud, and other cyberattacks.The contents of this book are appropriate for a wide range of readers from general readers to industry experts and scientists. Because it bridges the gap between generative AI and cybersecurity, experts from both fields will benefit from the information presented within. Students with a background in either area will also benefit from the approach that leads from general to specific applications.

Deep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design. Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as: Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learningPhysical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer securityMedium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell associationNetwork layer applications, covering traffic routing, network classification, and network slicingWith comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for researchers and communications engineers, along with developers and entrepreneurs in autonomous systems, who wish to harness this technology in practical applications.

Metaverse Communication and Computing Networks Understand the future of the Internet with this wide-ranging analysis “Metaverse” is the term for applications that allow users to assume digital avatars to interact with other humans and software functions in a three-dimensional virtual space. These applications and the spaces they create constitute an exciting and challenging new frontier in digital communication. Surmounting the technological and conceptual barriers to creating the Metaverse will require researchers and engineers familiar with its underlying theories and a wide range of technologies and techniques. Metaverse Communication and Computing Networks provides a comprehensive treatment of Metaverse theory and the technologies that can be brought to bear on this new pursuit. It begins by describing the Metaverse’s underlying architecture and infrastructure, physical and digital, before addressing how existing technologies are being adapted to its use. It concludes with an overview of the challenges facing the Metaverse. The result is a thorough introduction to a subject that may define the future of the internet. Metaverse Communication and Computing Networks readers will also find: Detailed treatment of technologies, including artificial intelligence, Virtual Reality, Extended Reality, and moreAnalysis of issues including data security, ethics, privacy, and social impactA real-world prototype for Metaverse applicationsMetaverse Communication and Computing Networks is a must-own for researchers and engineers looking to understand this growing area of technology, and entrepreneurs interested in establishing Metaverse businesses.

Comprehensive review of state-of-the-art research and development in Generative AI for future communications and networking Generative AI for Communications Systems provides a systematic foundation of knowledge on Generative AI for communications and networking. This book discusses the great potential and challenges in applying Generative AI as promising solutions to future communications systems and enables and facilitates “Generative AI as a Service” by exploring novel communications, networking architectures, protocols, and research trends. The book also includes information on: Crucial challenges to solve in Generative AI, such as training data availability, computational complexity, generalization for various scenarios, robustness of noisy and incomplete data, and real-time adaptation in communications and networking systemsCybersecurity concerns such as ethics and privacy in relation to Generative AIApplications of Generative AI across various layers, including the PHY layer, MAC layer, Network layer, and Application layerCommunications and networking solutions to meet the computing and communications challenges and demands to train and deploy large-scale Generative AI modelsGenerative AI for Communications Systems is an excellent up-to-date resource on the subject for scholars and researchers in the fields of communications, artificial intelligence, machine learning, and network optimization as well as professionals working in the communications industry including engineers, network architects, and system designers.

The latest advances in several emerging technologies such as AI, blockchain, privacy-preserving algorithms used in localization and positioning systems, cloud computing and computer vision all have great potential in facilitating social distancing. Benefits range from supporting people to work from home to monitoring micro- and macro- movements such as contact tracing apps using Bluetooth, tracking the movement and transportation level of a city and wireless positioning systems to help people keep a safe distance by alerting them when they are too close to each other or to avoid congestion. However, implementing such technologies in practical scenarios still faces various challenges.This book aims to lay the foundations of how these technologies could be adopted to realize and facilitate social distancing to better manage pandemics and future outbreaks. Starting with basic concepts, models and practical technology-based social distancing scenarios, the authors present enabling wireless technologies and solutions which could be widely adopted to encourage social distancing. They include symptom prediction, detection and monitoring of quarantined people and contact tracing. In the future, smart infrastructures for next-generation wireless systems should incorporate a pandemic mode in their standard architecture and design.