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实用机器学习

作者：孙亮 、黄倩

出版社：人民邮电出版社

ISBN：9787115446466

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Mastering Machine Learning for Penetration Testing 2018 评分:

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 Introduction to Machine Learning in Pentesting Phishing Domain Detection Malware Detection with API Calls and PE Headers Malware Detection with Deep Learning Botnet Detection with Machine Learning Machine Learning in Anomaly Detection Systems Detecting Advanced Persistent Threats Evading Intrusion Detection Systems with Adversarial Machine Learning Bypass machine learning malware Detectors Best Practices for Machine Learning and Feature Engineering Assessments 使用Python机器学习成为渗透测试的大师 主要特征 识别模糊性并破坏智能安全系统 执行独特的网络攻击以破坏强大的系统 学习利用机器学习算法 书说明 网络安全对企业和个人都至关重要。随着系统越来越智能化，我们现在看到机器学习中断了计算机安全性。随着即将到来的安全产品中机器学习的采用，对于测试人员和安全研究人员而言，了解这些系统如何工作以及为了测试目的而违反这些系统非常重要。 本书从机器学习的基础知识和用于构建健壮系统的算法开始。一旦您对安全产品如何利用机器学习有了一个公平的理解，您将深入探讨破坏此类系统的核心概念。通过实际使用案例，您将看到如何找到漏洞并超越自学安全系统。 当您完成章节后，您将专注于网络入侵检测和AV和IDS规避等主题。我们还将介绍识别模糊性的最佳实践，以及破坏智能系统的广泛技术。 在本书的最后，您将精通识别自学安全系统中的漏洞，并能够有效地破坏机器学习系统。 你会学到什么 深入了解机器学习 了解自然语言处理（NLP） 了解恶意软件功能工程 使用Python库构建生成对抗网络 通过机器学习和ELK堆栈开展威胁搜索工作 探索机器学习的最佳实践 这本书的用途是谁 本书适用于对学习打破智能安全系统技术感兴趣的笔式测试人员和安全专业人员。需要Python的基础知识，但不需要先前的机器学习知识。 目录 Pentesting中的机器学习简介 网络钓鱼域检测 使用API​​调用和PE标头进行恶意软件检测 深度学习的恶意软件检测 机器学习的僵尸网络检测 异常检测系统中的机器学习 检测高级持续性威胁 利用对抗机器学习规避入侵检测系统 绕过机器学习恶意软件探测器 机器学习和特征工程的最佳实践 评估

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