Machine Learning Security Principles: Keep data, networks, users, and applications safe from prying eyes

Thwart hackers by preventing, detecting, and misdirecting access before they can plant malware, obtain credentials, engage in fraud, modify data, poison models, corrupt users, eavesdrop, and otherwise ruin your day

Key Features

- Discover how hackers rely on misdirection and deep fakes to fool even the best security systems
- Retain the usefulness of your data by detecting unwanted and invalid modifications
- Develop application code to meet the security requirements related to machine learning

Book Description

Businesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning.

As you progress to the second part, you'll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references.

The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary's reputation. Once you've understood hacker goals and detection techniques, you'll learn about the ramifications of deep fakes, followed by mitigation strategies.

This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You'll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks.

By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.

What you will learn

- Explore methods to detect and prevent illegal access to your system
- Implement detection techniques when access does occur
- Employ machine learning techniques to determine motivations
- Mitigate hacker access once security is breached
- Perform statistical measurement and behavior analysis
- Repair damage to your data and applications
- Use ethical data collection methods to reduce security risks

Who this book is for

Whether you're a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. While most resources available on this topic are written in a language more suitable for experts, this guide presents security in an easy-to-understand way, employing a host of diagrams to explain concepts to visual learners. While familiarity with machine learning concepts is assumed, knowledge of Python and programming in general will be useful.

阻止駭客在他們植入惡意軟體、獲取憑證、從事詐騙、修改數據、破壞模型、損害用戶、竊聽等行為之前，通過預防、檢測和誤導來保護自己。

主要特點：
- 了解駭客如何依靠誤導和深度偽造來欺騙最佳安全系統
- 通過檢測不需要和無效的修改來保留數據的有用性
- 開發應用程式代碼以滿足與機器學習相關的安全要求

書籍描述：
企業正在利用人工智能的力量，使以前複雜且昂貴的工作變得更加簡單、快速和便宜。本書的第一部分將更深入地探討這些過程，幫助您了解安全在機器學習中的角色。

隨著您進入第二部分，您將更多地了解機器學習常用的環境，並通過代碼、圖形和現實世界的參考來深入研究困擾它們的安全威脅。

本書的下一部分將指導您在現代計算環境中檢測駭客行為的過程，其中詐騙在機器學習中有多種形式，從通過假評論獲得銷售到破壞對手的聲譽。一旦您了解了駭客的目標和檢測技術，您將學習深度偽造的影響，並採取相應的緩解策略。

本書還將帶您了解採用道德數據來源的最佳實踐，從而降低與數據相關的安全風險。您將看到從數據集中刪除個人身份信息（PII）的簡單行為如何降低社交工程攻擊的風險。

通過閱讀本機器學習書籍，您將對各種攻擊和保護機器學習系統的技術有更深入的了解。

您將學到什麼：
- 探索檢測和防止非法訪問系統的方法
- 當訪問發生時實施檢測技術
- 使用機器學習技術來確定動機
- 一旦安全被破壞，減輕駭客訪問
- 進行統計測量和行為分析
- 修復數據和應用程式的損壞
- 使用道德數據收集方法降低安全風險

本書適合對機器學習技術有所了解的數據科學家、研究人員或管理人員。儘管這個主題的大多數資源都是針對專家撰寫的，但本指南以易於理解的方式呈現安全性，並使用大量圖表來向視覺學習者解釋概念。雖然假設讀者對機器學習概念有所了解，但對Python和編程的一般知識將會有所幫助。

1. Defining Machine Learning Security
2. Mitigating Risk at Training by Validating and Maintaining Datasets
3. Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks
4. Considering the Threat Environment
5. Keeping Your Network Clean
6. Detecting and Analyzing Anomalies
7. Dealing with Malware
8. Locating Potential Fraud
9. Defending against Hackers
10. Considering the Ramifications of Deepfakes
11. Leveraging Machine Learning against Hacking
12. Embracing and Incorporating Ethical Behavior

1. 定義機器學習安全性
2. 通過驗證和維護數據集來減輕訓練中的風險
3. 通過避免對抗性機器學習攻擊來減輕推論風險
4. 考慮威脅環境
5. 保持網絡清潔
6. 檢測和分析異常
7. 處理惡意軟件
8. 找出潛在的欺詐行為
9. 防禦駭客攻擊
10. 考慮深偽技術的影響
11. 利用機器學習對抗駭客攻擊
12. 採納和融入道德行為