Malware Data Science: Attack Detection and Attribution
暫譯: 惡意軟體數據科學：攻擊檢測與歸因

Joshua Saxe, Hillary Sanders

Malware Data Science: Attack Detection and Attribution

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商品描述

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.

Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist.

In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis.

You'll learn how to:
- Analyze malware using static analysis
- Observe malware behavior using dynamic analysis
- Identify adversary groups through shared code analysis
- Catch 0-day vulnerabilities by building your own machine learning detector
- Measure malware detector accuracy
- Identify malware campaigns, trends, and relationships through data visualization

Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.

商品描述(中文翻譯)

《惡意程式資料科學》解釋了如何使用機器學習和資料視覺化來識別、分析和分類大規模的惡意程式。

安全性已成為一個「大數據」問題。惡意程式的增長速度已加速至每年數千萬個新檔案，而我們的網路每天產生的安全相關數據也在不斷增加。為了防禦這些先進的攻擊，您需要學會像資料科學家一樣思考。

在《惡意程式資料科學》中，安全資料科學家 Joshua Saxe 介紹了機器學習、統計學、社交網路分析和資料視覺化，並展示了如何將這些方法應用於惡意程式的檢測和分析。

您將學會如何：
- 使用靜態分析來分析惡意程式
- 使用動態分析來觀察惡意程式的行為
- 通過共享代碼分析來識別對手群體
- 通過建立自己的機器學習檢測器來捕捉 0-day 漏洞
- 測量惡意程式檢測器的準確性
- 通過資料視覺化來識別惡意程式活動、趨勢和關係

無論您是希望增強現有技能的惡意程式分析師，還是對攻擊檢測和威脅情報感興趣的資料科學家，《惡意程式資料科學》都將幫助您保持領先。

目錄大綱

Chapter 1: Basic Static Malware Analysis
Chapter 2: Beyond Basic Static Analysis: x86 Disassembly
Chapter 3: A Brief Introduction to Dynamic Analysis
Chapter 4: Identifying Attack Campaigns Using Malware Networks
Chapter 5: Shared Code Analysis
Chapter 6: Understanding Machine Learning-Based Malware Detectors
Chapter 7: Evaluating Malware Detection Systems
Chapter 8: Building Machine Learning Detectors
Chapter 9: Visualizing Malware Trends
Chapter 10: Deep Learning Basics
Chapter 11: Building a Neural Network Malware Detector with Keras
Chapter 12: Becoming a Data Scientist
Appendix: An Overview of Datasets and Tools

目錄大綱(中文翻譯)

Chapter 1: Basic Static Malware Analysis

Chapter 2: Beyond Basic Static Analysis: x86 Disassembly

Chapter 3: A Brief Introduction to Dynamic Analysis

Chapter 4: Identifying Attack Campaigns Using Malware Networks

Chapter 5: Shared Code Analysis

Chapter 6: Understanding Machine Learning-Based Malware Detectors

Chapter 7: Evaluating Malware Detection Systems

Chapter 8: Building Machine Learning Detectors

Chapter 9: Visualizing Malware Trends

Chapter 10: Deep Learning Basics

Chapter 11: Building a Neural Network Malware Detector with Keras

Chapter 12: Becoming a Data Scientist

Appendix: An Overview of Datasets and Tools

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