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\u003cp\u003e\u003cb\u003eThwart 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\u003c/b\u003e\u003c/p\u003e\u003ch4\u003eKey Features\u003c/h4\u003e\u003cul\u003e\u003cli\u003eDiscover how hackers rely on misdirection and deep fakes to fool even the best security systems\u003c/li\u003e\u003cli\u003eRetain the usefulness of your data by detecting unwanted and invalid modifications\u003c/li\u003e\u003cli\u003eDevelop application code to meet the security requirements related to machine learning\u003c/li\u003e\u003c/ul\u003e\u003ch4\u003eBook Description\u003c/h4\u003eBusinesses 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.\nAs you progress to the second part, youâll 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.\nThe 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 adversaryâs reputation. Once youâve understood hacker goals and detection techniques, youâll learn about the ramifications of deep fakes, followed by mitigation strategies.\nThis book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. Youâll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks.\nBy 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.\u003ch4\u003eWhat you will learn\u003c/h4\u003e\u003cul\u003e\u003cli\u003eExplore methods to detect and prevent illegal access to your system\u003c/li\u003e\u003cli\u003eImplement detection techniques when access does occur\u003c/li\u003e\u003cli\u003eEmploy machine learning techniques to determine motivations\u003c/li\u003e\u003cli\u003eMitigate hacker access once security is breached\u003c/li\u003e\u003cli\u003ePerform statistical measurement and behavior analysis\u003c/li\u003e\u003cli\u003eRepair damage to your data and applications\u003c/li\u003e\u003cli\u003eUse ethical data collection methods to reduce security risks\u003c/li\u003e\u003c/ul\u003e\u003ch4\u003eWho this book is for\u003c/h4\u003eWhether youâre 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. |
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