Название: Deep Learning Approaches for Security Threats in IoT Environments Автор: Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash Издательство: Wiley-IEEE Press Год: 2023 Страниц: 387 Язык: английский Формат: pdf (true) Размер: 19.1 MB
Deep Learning Approaches for Security Threats in IoT Environments An expert discussion of the application of deep learning methods in the IoT security environment. In Deep Learning Approaches for Security Threats in IoT Environments , a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation.
Why use ML for IoT security? Think about developing intrusion detection using conventional programming language: • First, you need to know how intrusion is performed and how the attack seems. You might notice that attackers generally look for vulnerabilities to get access without being identified. This can be performed by performing some actions that follow some pattern. • Next, you implement the detection program to identify these malicious patterns and warn you if something is detected. • Then, you test your program by repeating the previous steps many times till it becomes good enough.
Given the complexity of the problem, you can expect your program to become a long list of complicated rules that will be extremely time-consuming to maintain. On the other hand, ML algorithms can enable the development of a solution to automatically learn the normal patterns of system behavior and so can learn patterns related to intrusions. The program is to the point,simpler to sustain, and relatively more precise than the previous version.
Deep learning (DL) is an ML technology that creates deeper versions of neural networks (NNs) that imitate the composition and functionality of the human brain. Hierarchical learning and deep structured learning are alternative names to DL, which implies stacking a big number of hidden layers that nonlinearly process the data by transforming it into various stages of abstraction aiming to extract the important features and representations. In other words, DL offers a mathematical model that learns to map a set of input data into a particular output by learning the relationship between them.
Although ML and DL share many similarities, they are not mutually exclusive. When the dataset is small and well-curated, for example, ML could be beneficial because the data has been meticulously prepared. Data preparation necessitates the involvement of a human, which means that ML algorithms will not be able to extract information from huge and complicated datasets and would underfit. ML is sometimes referred to as “shallow learning” due to its ability to learn from minimal datasets. DL shows robust performance even when the dataset size is huge. DL is capable of deducing precise conclusions on its own from any set of data, no matter how complicated the pattern is.
This book applies Deep Learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues.
Readers also find:
A thorough introduction to Artificial Intelligence and the Internet of Things, including key concepts like Deep Learning, security, and privacy Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks In-depth examinations of the architectural design of cloud, fog, and edge computing networks Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks
Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying Deep Learning, cybersecurity, privacy preservation, and the security of IoT networks.
Скачать Deep Learning Approaches for Security Threats in IoT Environments
Intelligent Security Systems Название: Intelligent Security Systems: How Artificial Intelligence, Machine Learning and Data Science Work For and Against Computer Security Автор:...
Deep Learning Applications for Cyber Security Название: Deep Learning Applications for Cyber Security (Advanced Sciences and Technologies for Security Applications) Автор: Mamoun Alazab...
Информация
Посетители, находящиеся в группе Гости, не могут оставлять комментарии к данной публикации.
Навигация
Вход на сайт
Реклама
Deep Learning Approaches for Security Threats in IoT Environments 
Название: Deep Learning Approaches for Security Threats in IoT Environments
