机器学习与安全：数据与算法守护网络系统

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"《机器学习与安全：数据与算法保护系统》由Clarence Chio和David Freeman合著，是一部深度探讨在数字时代保障网络安全的关键著作。两位作者以其丰富的学术背景和实战经验，揭示了如何运用机器学习技术来应对大规模网络威胁，提升安全防护能力，以适应日益复杂的在线安全环境。书中涵盖了机器学习在网络安全中的最新理论研究，以及将这些理论应用于实际场景中的宝贵经验。它不仅阐述了异常检测、用户端保护等核心概念，还为读者提供了一个实用的指南，指导如何将机器学习技术应用于计算机系统的安全保障，以抵御各种恶意活动，包括但不限于网络攻击、欺诈和隐私侵犯。该书得到了业界知名人士的高度评价。Facebook首席安全官Alex Stamos赞誉它是定义未来网络安全趋势的关键之作，强调了机器学习在大规模防御中的关键作用。斯坦福大学计算机科学教授Dan Boneh也认为，对于任何希望了解机器学习在保障计算机系统安全中的应用的人来说，这是一本极其实用的教程。书中提供的是一份清晰的高清视角，让读者能够深入了解机器学习技术在保护系统中的具体实践操作。此外，来自Google的安全与隐私组织成员Nwokedi C.Idika博士也肯定了本书的专业性和实用性，称其为深入理解网络安全领域中机器学习技术的绝佳资源。《机器学习与安全》不仅适合网络安全专业人员，也对对技术感兴趣的普通读者极具参考价值，它通过深入浅出的方式，展示了如何利用数据驱动的算法构建更坚固的防护体系，确保用户在网络空间的安全和隐私得到充分保障。"

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xiv | Preface

to see unfiltered and undetected spam in their inboxes. These results demonstrate an

enormous advance over the simplistic spam filtering techniques developed in the

early days of the internet, which made use of simple word filtering and email meta‐

data reputation to achieve modest results.

The fundamental lesson that both researchers and practitioners have taken away from

this battle is the importance of using data to defeat malicious adversaries and improve

the quality of our interactions with technology. Indeed, the story of spam fighting

serves as a representative example for the use of data and machine learning in any

field of computer security. Today, almost all organizations have a critical reliance on

technology, and almost every piece of technology has security vulnerabilities. Driven

by the same core motivations as the spammers from the 1980s (unregulated, cost-free

access to an audience with disposable income and private information to offer), mali‐

cious actors can pose security risks to almost all aspects of modern life. Indeed, the

fundamental nature of the battle between attacker and defender is the same in all

fields of computer security as it is in spam fighting: a motivated adversary is con‐

stantly trying to misuse a computer system, and each side races to fix or exploit the

flaws in design or technique before the other uncovers it. The problem statement has

not changed one bit.

Computer systems and web services have become increasingly centralized, and many

applications have evolved to serve millions or even billions of users. Entities that

become arbiters of information are bigger targets for exploitation, but are also in the

perfect position to make use of the data and their user bases to achieve better security.

Coupled with the advent of powerful data crunching hardware and the development

of more powerful data analysis and machine learning algorithms, there has never

been a better time for exploiting the potential of machine learning in security.

In this book, we demonstrate applications of machine learning and data analysis tech‐

niques to various problem domains in security and abuse. We explore methods for

evaluating the suitability of different machine learning techniques in different scenar‐

ios, and focus on guiding principles that will help you use data to achieve better secu‐

rity. Our goal is not to leave you with the answer to every security problem you might

face, but rather to give you a framework for thinking about data and security as well

as a toolkit from which you can pick the right method for the problem at hand.

The remainder of this chapter sets up context for the rest of the book: we discuss

what threats modern computer and network systems face, what machine learning is,

and how machine learning applies to the aforementioned threats. We conclude with a

detailed examination of approaches to spam fighting, which provides a concrete

example of applying machine learning to security that can be generalized to nearly

any domain.

2 | Chapter 1: Why Machine Learning and Security?

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