Malware Data Science: Attack Detection and Attribution

Joshua Saxe, Hillary Sanders

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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)

《惡意軟體資料科學》解釋了如何使用機器學習和資料視覺化來識別、分析和分類大規模惡意軟體。

安全問題已成為一個「大數據」問題。惡意軟體的增長速度已加快到每年數千萬個新檔案,而我們的網絡每天都會產生越來越多與安全相關的數據洪流。為了防禦這些先進的攻擊,您需要知道如何像一個資料科學家一樣思考。

在《惡意軟體資料科學》中,安全資料科學家Joshua Saxe介紹了機器學習、統計學、社交網絡分析和資料視覺化,並向您展示如何應用這些方法來進行惡意軟體檢測和分析。

您將學習如何:
- 使用靜態分析分析惡意軟體
- 使用動態分析觀察惡意軟體行為
- 通過共享代碼分析識別對手組織
- 通過構建自己的機器學習檢測器捕捉0-day漏洞
- 測量惡意軟體檢測器的準確性
- 通過資料視覺化識別惡意軟體活動、趨勢和關係

無論您是一名惡意軟體分析師,希望增加技能,還是一名對攻擊檢測和威脅情報感興趣的資料科學家,《惡意軟體資料科學》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)

第1章:基本靜態惡意軟體分析
第2章:超越基本靜態分析:x86反組譯
第3章:動態分析簡介
第4章:使用惡意軟體網路識別攻擊活動
第5章:共享程式碼分析
第6章:了解基於機器學習的惡意軟體偵測器
第7章:評估惡意軟體偵測系統
第8章:建立機器學習偵測器
第9章:視覺化惡意軟體趨勢
第10章:深度學習基礎
第11章:使用Keras建立神經網路惡意軟體偵測器
第12章:成為資料科學家
附錄:資料集和工具概述