机器学习与安全(影印版)(英文版)

机器学习技术能够解决计算机安全问题，并最 终为攻防双方之间的猫鼠游戏画上一个句号吗?或者 说这只是炒作?现在你可以深入这一学科，自己回答 这个问题了!有了《机器学习与安全(影印版)(英文 版)》这本实用指南，你就可以探索如何将机器学习 应用于各种安全问题(如入侵检测、恶意软件分类和 网络分析)。 机器学习和安全专家克拉伦斯·奇奥与大卫· 弗里曼为讨论这两个领域之间的联姻提供了框架， 另外还包括一个机器学习算法工具箱，你可以将其 应用于一系列安全问题。本书适合于安全工程师和 数据科学家。

Preface. 1. Why Machine Learning and Security? Cyber Threat Landscape The Cyber Attacker's Economy A Marketplace for Hacking Skills Indirect Monetization The Upshot What Is Machine Learning? What Machine Learning Is Not Adversaries Using Machine Learning Real-World Uses of Machine Learning in Security Spam Fighting: An Iterative Approach Limitations of Machine Learning in Security 2. Classifying and Clustering Machine Learning: Problems and Approaches Machine Learning in Practice: A Worked Example Training Algorithms to Learn Model Families Loss Functions Optimization Supervised Classification Algorithms Logistic Regression Decision Trees Decision Forests Support Vector Machines Naive Bayes k-Nearest Neighbors Neural Networks Practical Considerations in Classification Selecting a Model Family Training Data Construction Feature Selection Overfitting and Underfitting Choosing Thresholds and Comparing Models Clustering Clustering Algorithms Evaluating Clustering Results Conclusion 3.Anomaly Detection When to Use Anomaly Detection Versus Supervised Learning Intrusion Detection with Heuristics Data-Driven Methods Feature Engineering for Anomaly Detection Host Intrusion Detection Network Intrusion Detection Web Application Intrusion Detection In Summary Anomaly Detection with Data and Algorithms Forecasting (Supervised Machine Learning) Statistical Metrics