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We live in a digital world, where we use digital tools and smart devices to communicate over the Internet. In turn, an enormous amount of data gets generated. The traditional computing architectures are inefficient in storing and managing this massive amount of data. Unfortunately, the data cannot be ignored as it helps businesses to make better decisions, solve problems, understand performance, improve processes, and understand customers. Therefore, we need modern systems capable of handling and managing data efficiently. In the past few decades, many distributed computing paradigms have emerged, and we have noticed a substantial growth in the applications based on such emerging paradigms. Some well-known emerging computing paradigms include cloud computing, fog computing, and edge computing, which have leveraged the increase in the volume of data being generated every second. However, the distributed computing paradigms face critical challenges, including network management and cyber security. We have witnessed the development of various networking models-IoT, SDN, and ICN-to support modern systems requirements.
However, they are undergoing rapid changes and need special attention. The main issue faced by these paradigms is that traditional solutions cannot be directly applied to address the challenges. Therefore, there is a significant need to develop improved network management and cyber security solutions. To this end, this book highlights the challenges faced by emerging paradigms and presents the recent developments made to address the challenges. More specifically, it presents a detailed study on security issues in distributed computing environments and their possible solutions, followed by applications of medical IoT, Deep Learning, IoV, healthcare, etc.
Edge computing-enabled secure information-centric networking: Privacy challenges, benefits, and future trends
Weighted attack graphs and behavioral cyber game theory for cyber risk quantification
NetFlow-based botnet detection in IoT edge environment using ensemble gradient boosting machine learning framework
Exploring the possibility of blockchain and smart contract-based digital certificate
Senso Scale: A framework to preserve privacy over cloud using sensitivity range
Addressing the cybersecurity issues in cloud computing
Role of medical image encryption algorithms in cloud platform for teleradiology applications
Machine-learning approach for detecting cyberattacks in Medical Internet of Things
Secure IoV-enabled systems at Fog Computing: Layout, security, and optimization algorithms and open issues
A capability maturity model and value judgment systems for a distributed network of ethical and context aware digital twin agents
A detailed cram on artificial intelligence industrial systems 4.0
Ensuring liveliness property in safety-critical systems
Machine learning for intelligent analytics
Secure 3D route optimization of combat vehicles in war field using IoV
Healthcare therapy for treating teenagers with internet addiction using behavioral patterns and neuro-feedback analysis
Containerization in cloud computing for OS-level virtualization
An adaptive deep learning approach for stock price forecasting