Deep Learning Approaches for Security Threats in Iot Environments (Hardcover)

Abdel-Basset, Mohamed, Moustafa, Nour, Hawash, Hossam

  • 出版商: Wiley
  • 出版日期: 2022-12-08
  • 售價: $1,980
  • 貴賓價: 9.8$1,940
  • 語言: 英文
  • 頁數: 384
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1119884144
  • ISBN-13: 9781119884149
  • 相關分類: DeepLearning物聯網 IoT資訊安全
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商品描述

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.

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 cyber security issues.

Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They'll 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 undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.

商品描述(中文翻譯)

在《深度學習方法在物聯網安全環境中的應用》一書中,一群傑出的網絡安全教育者提供了對如何處理和評估物聯網系統和網絡安全的深入探討。在這本書中,讀者將研究人工智能(AI)和物聯網的關鍵概念,並應用有效的策略來保護物聯網網絡。作者們討論了監督、半監督和無監督的深度學習技術,以及用於隱私保護的強化和聯邦學習方法。

本書將深度學習方法應用於物聯網網絡,解決了專業人士在物聯網領域工作時經常遇到的安全問題,並提供了智能設備解決網絡安全問題的方法。

讀者還可以訪問附帶的網站,其中包含PowerPoint演示文稿、支持視頻的鏈接和其他資源。他們還將找到:

- 對人工智能和物聯網的全面介紹,包括深度學習、安全和隱私等關鍵概念
- 對構建現代物聯網系統和網絡安全的深度學習的架構、協議和標準的全面討論
- 對雲端、霧計算和邊緣計算網絡的架構設計的深入研究
- 對物聯網網絡相關的安全需求、威脅和對策的詳細介紹

《深度學習方法在物聯網安全環境中的應用》非常適合從事人工智能、網絡安全和物聯網行業的專業人士,同時也適合深度學習、網絡安全、隱私保護和物聯網網絡安全的本科和研究生學生閱讀。

作者簡介

Mohamed Abdel-Basset, PhD, is an Associate Professor in the Faculty of Computers and Informatics at Zagazig University, Egypt. He is a Senior Member of the IEEE.

Nour Moustafa, PhD, is a Postgraduate Discipline Coordinator (Cyber) and Senior Lecturer in Cybersecurity and Computing at the School of Engineering and Information Technology at the University of New South Wales, UNSW Canberra, Australia.

Hossam Hawash is an Assistant Lecturer in the Department of Computer Science, Faculty of Computers and Informatics at Zagazig University, Egypt.

作者簡介(中文翻譯)

Mohamed Abdel-Basset, PhD, 是埃及扎加齊格大學計算機與資訊學院的副教授。他是IEEE的高級會員。

Nour Moustafa, PhD, 是澳大利亞新南威爾斯大學坎培拉分校工程與資訊技術學院的研究生學科協調員(網絡安全)和高級講師。

Hossam Hawash是埃及扎加齊格大學計算機與資訊學院的助理講師。