Forthcoming Networks and Sustainability in the Aiot Era: Second International Conference Fones-Aiot 2024 - Volume 2

Rasheed, Jawad, Abu-Mahfouz, Adnan M., Fahim, Muhammad

  • 出版商: Springer
  • 出版日期: 2024-06-26
  • 售價: $9,430
  • 貴賓價: 9.5$8,959
  • 語言: 英文
  • 頁數: 424
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031628802
  • ISBN-13: 9783031628801
  • 相關分類: 物聯網 IoT
  • 海外代購書籍(需單獨結帳)

商品描述

This book introduces a groundbreaking approach to enhancing IoT device security, providing a comprehensive overview of its applications and methodologies. Covering a wide array of topics, from crime prediction to cyberbullying detection, from facial recognition to analyzing email spam, it addresses diverse challenges in contemporary society. Aimed at researchers, practitioners, and policymakers, this book equips readers with practical tools to tackle real-world issues using advanced machine learning algorithms. Whether you're a data scientist, law enforcement officer, or urban planner, this book is a valuable resource for implementing predictive models and enhancing public safety measures. It is a comprehensive guide for implementing machine learning solutions across various domains, ensuring optimal performance and reliability. Whether you're delving into IoT security or exploring the potential of AI in urban landscapes, this book provides invaluable insights and tools to navigate the evolving landscape of technology and data science.

The book provides a comprehensive overview of the challenges and solutions in contemporary cybersecurity. Through case studies and practical examples, readers gain a deeper understanding of the security concerns surrounding IoT devices and learn how to mitigate risks effectively. The book's interdisciplinary approach caters to a diverse audience, including academics, industry professionals, and government officials, who seek to address the growing cybersecurity threats in IoT environments. Key uses of this book include implementing robust security measures for IoT devices, conducting research on machine learning algorithms for attack detection, and developing policies to enhance cybersecurity in IoT ecosystems. By leveraging advanced machine learning techniques, readers can effectively detect and mitigate cyber threats, ensuring the integrity and reliability of IoT systems. Overall, this book is a valuable resource for anyone involved in designing, implementing, or regulating IoT devices and systems.

商品描述(中文翻譯)

本書介紹了一種突破性的方法來增強物聯網設備的安全性,提供了其應用和方法論的全面概述。從犯罪預測到網絡欺凌檢測,從人臉識別到分析電子郵件垃圾郵件,涵蓋了當代社會中的各種挑戰。本書針對研究人員、從業人員和政策制定者,為讀者提供了使用先進的機器學習算法解決現實問題的實用工具。無論您是數據科學家、執法人員還是城市規劃師,本書都是實施預測模型和增強公共安全措施的寶貴資源。它是在各個領域實施機器學習解決方案的全面指南,確保最佳性能和可靠性。無論您是深入研究物聯網安全還是探索人工智能在城市景觀中的潛力,本書都提供了寶貴的見解和工具,以應對不斷變化的技術和數據科學領域。

本書全面概述了當代網絡安全的挑戰和解決方案。通過案例研究和實際示例,讀者可以更深入地了解圍繞物聯網設備的安全問題,並學習如何有效地降低風險。本書的跨學科方法適合各種讀者,包括學術界、行業專業人士和政府官員,他們希望應對物聯網環境中不斷增長的網絡安全威脅。本書的關鍵用途包括為物聯網設備實施強大的安全措施,研究用於攻擊檢測的機器學習算法,以及制定增強物聯網生態系統網絡安全的政策。通過利用先進的機器學習技術,讀者可以有效地檢測和降低網絡威脅,確保物聯網系統的完整性和可靠性。總之,本書對於任何參與設計、實施或監管物聯網設備和系統的人士都是一個寶貴的資源。