Machine Learning Security Principles: Keep data, networks, users, and applicatio

Description: Machine Learning Security Principles by John Paul Mueller, Rod Stephens Estimated delivery 3-12 business days Format Paperback Condition Brand New Description As hackers come up with new ways to mangle or misdirect data in nearly undetectable ways to obtain access, skew calculations, and modify outcomes. Machine Learning Security Principles helps you understand hacker motivations and techniques in an easy-to-understand way. Publisher Description 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 dayKey FeaturesDiscover how hackers rely on misdirection and deep fakes to fool even the best security systemsRetain the usefulness of your data by detecting unwanted and invalid modificationsDevelop application code to meet the security requirements related to machine learningBook DescriptionBusinesses 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, youll 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 adversarys reputation. Once youve understood hacker goals and detection techniques, youll 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. Youll 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, youll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.What you will learnExplore methods to detect and prevent illegal access to your systemImplement detection techniques when access does occurEmploy machine learning techniques to determine motivationsMitigate hacker access once security is breachedPerform statistical measurement and behavior analysisRepair damage to your data and applicationsUse ethical data collection methods to reduce security risksWho this book is forWhether youre 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. Author Biography John Paul Mueller is a seasoned author and technical editor. He has writing in his blood, having produced 121 books and more than 600 articles to date. The topics range from networking to artificial intelligence and from database management to heads-down programming. Some of his current books include discussions of data science, machine learning, and algorithms. He also writes about computer languages such as C++, C#, and Python. His technical editing skills have helped more than 70 authors refine the content of their manuscripts. John has provided technical editing services to a variety of magazines, performed various kinds of consulting, and he writes certification exams. Rod Stephens has been a software developer, consultant, instructor, and author. He has written more than 30 books and 250 magazine articles covering such topics as three-dimensional graphics, algorithms, database design, software engineering, interview puzzles, C#, and Visual Basic. Rods popular C# Helper and VB Helper websites receive millions of hits per year and contain thousands of tips, tricks, and example programs for C# and Visual Basic developers. Details ISBN 1804618853 ISBN-13 9781804618851 Title Machine Learning Security Principles Author John Paul Mueller, Rod Stephens Format Paperback Year 2022 Pages 450 Publisher Packt Publishing Limited GE_Item_ID:139703467; About Us Grand Eagle Retail is the ideal place for all your shopping needs! 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