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。
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书名 | 机器学习与安全(影印版)(英文版) |
分类 | |
作者 | (美)克拉伦斯·奇奥//大卫·弗里曼 |
出版社 | 东南大学出版社 |
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简介 | 内容推荐 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。 目录 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 Goodness-of-Fit Unsupervised Machine Learning Algorithms Density-Based Methods In Summary Challenges of Using Machine Learning in Anomaly Detection Response and Mitigation Practical System Design Concerns Optimizing for Explainability Maintainability of Anomaly Detection Systems Integrating Human Feedback Mitigating Adversarial Effects Conclusion 4. Malware Analysis Understanding Malware Defining Malware Classification Malware: Behind the Scenes Feature Generation Data Collection Generating Features Feature Selection From Features to Classification How to Get Malware Samples and Labels Conclusion 5. Network Traffic Analysis Theory of Network Defense Access Control and Authentication Intrusion Detection Detecting In-Network Attackers Data-Centric Security Honeypots Summary Machine Learning and Network Security From Captures to Features Threats in the Network Botnets and You Building a Predictive Model to Classify Network Attacks Exploring the Data Data Preparation Classification Supervised Learning Semi-Supervised Learning Unsupervised Learning Advanced Ensembling Conclusion 6. Protecting the Consumer Web Monetizing the Consumer Web Types of Abuse and the Data That Can Stop Them Authentication and Account Takeover Account Creation Financial Fraud Bot Activity Supervised Learning for Abuse Problems Labeling Data Cold Start Versus Warm Start False Positives and False Negatives Multiple Responses Large Attacks Clustering Abuse Example: Clustering Spam Domains Generating Clusters Scoring Clusters Further Directions in Clustering Conclusion 7. Production Systems Defining Machine Learning System Maturity and Scalability What's Important for Security Machine Learning Systems? Data Quality Problem: Bias in Datasets Problem: Label Inaccuracy Solutions: Data Quality Problem: Missing Data Solutions: Missing Data Model Quality Problem: Hyperparameter Optimization Solutions: Hyperparameter Optimizati |
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