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.

由领先的研究人员撰写，这篇完整的介绍汇集了在敌对环境中构建健壮的机器学习所需的所有理论和工具。发现当对手主动毒害数据以操纵统计推断时，机器学习系统如何适应，学习调查系统安全性和执行稳健数据分析的最新实用技术，并深入了解针对最新一波网络攻击设计有效对策的新方法。详细讨论了隐私保护机制和分类器的近似最优规避，深入的垃圾邮件和网络安全案例研究突出了对传统机器学习算法的成功攻击。