Название: Next-Generation Systems and Secure Computing
Автор: Subhabrata Barman, Santanu Koley, Subhankar Joardar
Издательство: Wiley-Scrivener
Год: 2025
Страниц: 474
Язык: английский
Формат: pdf (true)
Размер: 37.5 MB

Next-Generation Systems and Secure Computing is essential for anyone looking to stay ahead in the rapidly evolving landscape of technology. It offers crucial insights into advanced computing models and their security implications, equipping readers with the knowledge needed to navigate the complex challenges of today’s digital world.

The development of technology in recent years has produced a number of scientific advancements in sectors like computer science. The advent of new computing models has been one particular development within this sector. New paradigms are always being invented, greatly expanding cloud computing technology. Fog, edge, and serverless computing are examples of these revolutionary advanced technologies. Nevertheless, these new approaches create new security difficulties and are forcing experts to reassess their current security procedures. Devices for edge computing aren’t designed with the same IT hardware protocols in mind. There are several application cases for edge computing and the Internet of Things (IoT) in remote locations. Yet, cybersecurity settings and software upgrades are commonly disregarded when it comes to preventing cybercrime and guaranteeing data privacy.

Next-Generation Systems and Secure Computing compiles cutting-edge studies on the development of cutting-edge computing technologies and their role in enhancing current security practices. The book will highlight topics like fault tolerance, federated cloud security, and serverless computing, as well as security issues surrounding edge computing in this context, offering a thorough discussion of the guiding principles, operating procedures, applications, and unexplored areas of study. Next-Generation Systems and Secure Computing is a one-stop resource for learning about the technology, procedures, and individuals involved in next-generation security and computing.

Deep Learning and Artificial Intelligence (AI) have recently emerged as game-changing technologies with the ability to completely change malware analysis and detection. A branch of Machine Learning known as Deep Learning focuses on using neural networks to directly learn intricate patterns and representations from unprocessed data. On the other hand, AI covers a wider range of algorithms and methods that try to imitate human intellect, making it perfect for addressing complex and dynamic cybersecurity concerns. When used for malware analysis, Deep Learning and AI provide several benefits over the conventional methods. The first benefit of using these approaches is that they make it possible to build complex models that can distinguish between different types of malware based on learned patterns rather than on pre-established criteria. These models may continuously change and successfully fend off new dangers because of their versatility. Deep learning and AI also equip malware analysts with effective tools to manage enormous volumes of data, enabling a scalable and real-time approach to quickly detect and address threats.

In this research work, an Arduino UNO microcontroller (SI-AUNO) is used to establish a smart irrigation system. Sensors assess soil humidity and wetness to estimate irrigation needs. The technology automates irrigation to maximize agricultural production and preserve water. Food demand rises with population and urbanization. Farmers are seeking effective irrigation technologies to maximize agricultural yields to fulfill this demand. The answer is smart irrigation systems, which provide real-time soil moisture data and reduce water waste. The suggested system employs low-cost sensors to monitor soil moisture and weather to predict plant water needs. Small farmers may use the cost-effective, easy-to-implement technology. The article details the system’s hardware, software, and testing and assessment findings. The smart irrigation system may increase agricultural production and water savings. This chapter shows how low-cost sensors and microcontroller technologies may increase irrigation efficiency and efficacy in smart irrigation systems. The suggested approach should boost agricultural production and water saving. Farmers may also be certain that the IoT-based smart irrigation system’s data, infrastructure, and control mechanisms are secure, assuring its dependability and longevity.