Adversarial Machine Learning (Hardcover)

Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, J. D. Tygar

  • 出版商: Cambridge
  • 出版日期: 2019-02-21
  • 售價: $1,560
  • 貴賓價: 9.8$1,529
  • 語言: 英文
  • 頁數: 338
  • 裝訂: Hardcover
  • ISBN: 1107043468
  • ISBN-13: 9781107043466
  • 相關分類: Machine Learning
  • 相關翻譯: 對抗機器學習 (簡中版)
  • 立即出貨 (庫存=1)

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商品描述

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.

商品描述(中文翻譯)

由領先的研究人員撰寫,這本完整的介紹將所有建立在對抗環境中的強健機器學習所需的理論和工具匯集在一起。了解機器學習系統如何在對手積極操縱數據以操縱統計推斷時進行適應,學習最新的實用技術來調查系統安全性並進行強健的數據分析,並深入了解設計有效對抗最新一波網絡攻擊的新方法。詳細討論了保護隱私的機制和接近最佳的分類器逃避,並通過深入案例研究介紹了對傳統機器學習算法的成功攻擊,如電子郵件垃圾郵件和網絡安全。提供了該領域目前最新技術的全面概述和可能的未來發展方向,這本開創性的著作對於計算機安全和機器學習的研究人員、從業人員和學生以及那些想要了解下一階段的網絡安全競爭的人來說是必讀之作。