Textbook in PDF format

This book offers the latest research results in security and privacy for Intelligent Edge Computing Systems. It presents state-of-the art content and provides an in-depth overview of the basic background in this related field. Practical areas in both security and risk analysis are addressed as well as connections directly linked to Edge Computing paradigms. This book also offers an excellent foundation on the fundamental concepts and principles of security, privacy and risk analysis in Edge Computation infrastructures. It guides the reader through the core ideas with relevant ease.
Edge Computing has burst onto the computational scene offering key technologies for allowing more flexibility at the edge of networks. As Edge Computing has evolved as well as the need for more in-depth solutions in security, privacy and risk analysis at the edge. This book includes various case studies and applications on Edge Computing. It includes the Internet of Things related areas, such as smart cities, blockchain, mobile networks, federated learning, cryptography and cybersecurity.
Machine learning (ML) has advanced drastically over the last two decades, from laboratory curiosity to a practical technology in widespread commercial use. However, recent works have shown that ML is a promising technology to identify important characteristics from a complex data set and reveal their importance. Federated Learning (FL) is an emerging ML technology that enables devices to learn collaboratively from a shared model. FL incorporates the basis of focused data collection, minimization, easing the privacy risks and costs resulting from traditional, centralized ML approaches. FL can train a model, leveraging the personal data of patients without ever sharing it with other entities. Although FL is designed for securing privacy risks of individuals, we may face unique challenges like efficient communication across the federated network, managing heterogeneous systems in the same network. Moreover, communication connectivity issues are resolved by offloading the excessive computational task from IoT devices to Edge nodes by considering federation and complex resource management in real-time. Edge computing assists in rapid processing and low latency concerns of intelligent IoT applications can be achieved.
This book is one of the first reference books covering security and risk analysis in Edge Computing Systems. Researchers and advanced-level students studying or working in Edge Computing and related security fields will find this book useful as a reference. Decision makers, managers and professionals working within these fields will want to purchase this book as well.
A Comprehensive Review on Edge Computing, Applications & Challenges
Big Data Analytics and Security Over the Cloud: Characteristics, Analytics, Integration and Security
Federated Learning Enabled Edge Computing Security for Internet of Medical Things: Concepts, Challenges and Open Issues
Embedded Edge and Cloud Intelligence
The Analysis on Impact of Cyber Security Threats on Smart Grids
Intelligent Intrusion Detection Algorithm Based on Multi-Attack for Edge-Assisted Internet of Things
Secure Data Analysis and Data Privacy
A Novel Trust Evaluation and Reputation Data Management Based Security System Model for Mobile Edge Computing Network
Network Security System in Mobile Edge Computing-to-IoMT Networks Using Distributed Approach
Wireless and Mobile Security in Edge Computing
An Intelligent Facial Expression Recognizer Using Modified ResNet-110 Using Edge Computing
Blockchain Based Simulated Virtual Machine Placement Hybrid Approach for Decentralized Cloud and Edge Computing Environments