Mastering Machine Learning for Penetration Testing

Become a master at penetration testing using machine learning with Python

Key Features

• Identify ambiguities and breach intelligent security systems
• Perform unique cyber attacks to breach robust systems
• Learn to leverage machine learning algorithms

Book Description

Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it's important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes.

This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you've gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you'll see how to find loopholes and surpass a self-learning security system.

As you make your way through the chapters, you'll focus on topics such as network intrusion detection and AV and IDS evasion. We'll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system.

By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system.

What you will learn

• Take an in-depth look at machine learning
• Get to know natural language processing (NLP)
• Understand malware feature engineering
• Build generative adversarial networks using Python libraries
• Work on threat hunting with machine learning and the ELK stack
• Explore the best practices for machine learning

Who this book is for

This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

Table of Contents

1. Introduction to Machine Learning in Pentesting
2. Phishing Domain Detection
3. Malware Detection with API Calls and PE Headers
4. Malware Detection with Deep Learning
5. Botnet Detection with Machine Learning
6. Machine Learning in Anomaly Detection Systems
7. Detecting Advanced Persistent Threats
8. Evading Intrusion Detection Systems with Adversarial Machine Learning
9. Bypass machine learning malware Detectors
10. Best Practices for Machine Learning and Feature Engineering
11. Assessments

成為使用Python的機器學習滲透測試大師

主要特點：

- 辨識模糊點並入侵智能安全系統
- 進行獨特的網絡攻擊以入侵強大的系統
- 學習利用機器學習算法

書籍描述：

網絡安全對於企業和個人都至關重要。隨著系統變得更加智能，我們現在看到機器學習干擾計算機安全。隨著機器學習在即將推出的安全產品中的應用，測試人員和安全研究人員了解這些系統的工作原理並對其進行入侵以進行測試變得重要。

本書從機器學習的基礎知識和用於構建強大系統的算法開始。一旦您對安全產品如何利用機器學習有了相當的了解，您將深入探討入侵此類系統的核心概念。通過實際案例，您將了解如何找到漏洞並超越自學安全系統。

在閱讀本書的過程中，您將專注於網絡入侵檢測、防病毒和入侵檢測系統的規避。我們還將介紹識別模糊點的最佳實踐以及入侵智能系統的廣泛技術。

通過閱讀本書，您將熟悉如何識別自學安全系統中的漏洞，並能夠高效地入侵機器學習系統。

您將學到什麼：

- 深入研究機器學習
- 了解自然語言處理（NLP）
- 理解惡意軟件特徵工程
- 使用Python庫構建生成對抗網絡
- 使用機器學習和ELK堆棧進行威脅狩獵
- 探索機器學習的最佳實踐

本書適合對破解智能安全系統技術感興趣的測試人員和安全專業人員。需要基本的Python知識，但不需要機器學習的先備知識。

目錄：

1. 機器學習在滲透測試中的介紹
2. 偵測釣魚網域
3. 使用API調用和PE標頭進行惡意軟件檢測
4. 使用深度學習進行惡意軟件檢測
5. 使用機器學習進行殭屍網絡檢測
6. 異常檢測系統中的機器學習
7. 檢測高級持續性威脅
8. 使用對抗機器學習規避入侵檢測系統
9. 繞過機器學習惡意軟件檢測器
10. 機器學習和特徵工程的最佳實踐
11. 評估