機器學習與

《機器學習與》是2018年東南大學出版社出版的圖書。

基本介紹

• 中文名：機器學習與
• 作者：Clarence Chio、David Freeman
• 類別：機器學習
• 出版社：東南大學出版社
• 出版時間：2018年11月
• 開本：16 開
• 裝幀：平裝
• ISBN：9787564179793

內容簡介

機器學習技術能夠解決計算機安全問題，並*終為攻防雙方之間的貓鼠遊戲畫上一個句號嗎?或者說這只是炒作?現在你可以深入這一學科，自己回答這個問題了!有了《機器學習與安全(影印版)(英文版)》這本實用指南，你就可以探索如何將機器學習套用於各種安全問題(如入侵檢測、惡意軟體分類和網路分析)。

機器學習和安全專家克拉倫斯·奇奧與大衛·弗里曼為討論這兩個領域之間的聯姻提供了框架，另外還包括一個機器學習算法工具箱，你可以將其套用於一系列安全問題。本書適合於安全工程師和數據科學家。

圖書目錄

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 Optimization

Feature: Feedback Loops, A/B Testing of Models

Feature: Repeatable and Explainable Results

Performance

Goal: Low Latency, High Scalability

Performance Optimization

Horizontal Scaling with Distributed Computing Frameworks

Using Cloud Services

Maintainability

Problem: Checkpointing, Versioning, and Deploying Models

Goal: Graceful Degradation

Goal: Easily Tunable and Configurable

Monitoring and Alerting

Security and Reliability

Feature: Robustness in Adversarial Contexts

Feature: Data Privacy Safeguards and Guarantees

Feedback and Usability

Conclusion

8. Adversarial Machine Learning

Terminology

The Importance of Adversarial ML

Security Vulnerabilities in Machine Learning Algorithms

Attack Transferability

Attack Technique: Model Poisoning

Example: Binary Classifier Poisoning Attack

Attacker Knowledge

Defense Against Poisoning Attacks

Attack Technique: Evasion Attack

Example: Binary Classifier Evasion Attack

Defense Against Evasion Attacks

Conclusion

A. Supplemental Material for Chapter 2

B. Integrating Open Source Intelligence

Index