Implementing Generative AI in Cybersecurity: Techniques, Tools, and Case Studies

Vemula, Anand

  • 出版商: Independently Published
  • 出版日期: 2024-06-01
  • 售價: $850
  • 貴賓價: 9.5$808
  • 語言: 英文
  • 頁數: 68
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798327273948
  • ISBN-13: 9798327273948
  • 相關分類: 人工智慧資訊安全
  • 海外代購書籍(需單獨結帳)

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商品描述


In an era where cyber threats are becoming increasingly sophisticated, "Implementing Generative AI in Cybersecurity: Techniques, Tools, and Case Studies" serves as a comprehensive guide for professionals and enthusiasts looking to leverage the power of generative AI to bolster their cybersecurity defenses. This book delves into the intersection of two rapidly evolving fields-artificial intelligence and cybersecurity-providing readers with the knowledge and tools necessary to stay ahead of cyber adversaries.

The book begins with an introduction to generative AI and its pivotal role in transforming cybersecurity. It covers the basics of generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), explaining their mechanics and applications in creating synthetic data, enhancing training datasets, and anonymizing sensitive information.

Moving into practical applications, the book explores how generative AI can be used for data augmentation and synthesis to improve the accuracy and robustness of machine learning models used in threat detection and incident response. Readers will learn about the latest techniques for detecting and defending against adversarial attacks, ensuring their AI systems remain resilient against sophisticated manipulations.

A significant portion of the book is dedicated to real-world case studies, demonstrating how leading organizations in various sectors-finance, healthcare, and government-have successfully implemented generative AI solutions to enhance their cybersecurity posture. These case studies provide valuable insights into the practical challenges and strategies for integrating AI technologies into existing security frameworks.

Deepfake detection and prevention, a crucial aspect of modern cybersecurity, is also covered in depth. The book outlines state-of-the-art detection techniques and countermeasures to combat the rising threat of synthetic media used for malicious purposes.

The use of natural language processing (NLP) in security is another focal point, highlighting its applications in phishing detection, secure communication analysis, and threat intelligence. Ethical considerations, privacy concerns, and the regulatory landscape are discussed to provide a holistic view of the challenges and responsibilities involved in deploying AI-driven cybersecurity solutions.

"Implementing Generative AI in Cybersecurity: Techniques, Tools, and Case Studies" is an essential resource for cybersecurity professionals, AI practitioners, and anyone interested in the future of digital security, offering practical guidance and actionable insights to navigate the complexities of integrating generative AI into cybersecurity strategies.

商品描述(中文翻譯)

在一個網路威脅日益複雜的時代,《在網路安全中實施生成式人工智慧:技術、工具與案例研究》為專業人士和愛好者提供了一本全面的指南,幫助他們利用生成式人工智慧的力量來加強網路安全防禦。本書深入探討了兩個快速發展領域的交集——人工智慧和網路安全,為讀者提供了必要的知識和工具,以便在網路對手面前保持優勢。

本書首先介紹了生成式人工智慧及其在轉變網路安全中的關鍵角色。它涵蓋了生成模型的基本概念,如生成對抗網路(GANs)和變分自編碼器(VAEs),解釋了它們的運作原理及在創建合成數據、增強訓練數據集和匿名化敏感信息中的應用。

進入實際應用部分,本書探討了如何利用生成式人工智慧進行數據增強和合成,以提高用於威脅檢測和事件響應的機器學習模型的準確性和穩健性。讀者將了解最新的技術,以檢測和防禦對抗性攻擊,確保其人工智慧系統在面對複雜操控時仍能保持韌性。

本書的一個重要部分專注於現實世界的案例研究,展示了金融、醫療和政府等各個領域的領先組織如何成功實施生成式人工智慧解決方案,以增強其網路安全態勢。這些案例研究提供了有關將人工智慧技術整合到現有安全框架中的實際挑戰和策略的寶貴見解。

深偽檢測和預防,作為現代網路安全的一個關鍵方面,也得到了深入探討。本書概述了最先進的檢測技術和對策,以應對用於惡意目的的合成媒體日益上升的威脅。

自然語言處理(NLP)在安全領域的應用也是一個重點,突顯了其在釣魚檢測、安全通信分析和威脅情報中的應用。倫理考量、隱私問題和監管環境也被討論,以提供對於部署人工智慧驅動的網路安全解決方案所涉及的挑戰和責任的全面視角。

《在網路安全中實施生成式人工智慧:技術、工具與案例研究》是網路安全專業人士、人工智慧從業者以及任何對數位安全未來感興趣的人的重要資源,提供了實用的指導和可行的見解,以應對將生成式人工智慧整合到網路安全策略中的複雜性。