Introduction to machine learning with applications in information security
著者
書誌事項
Introduction to machine learning with applications in information security
（Chapman & Hall/CRC machine learning & pattern recognition series）（A Chapman & Hall book）
CRC Press, c2018
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注記
Bibliography: p. 319-337
Includes index
内容説明・目次
内容説明
Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn't prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts.
The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis.
Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book.
Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader's benefit, the figures in the book are also available in electronic form, and in color.
About the Author
Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. He received his Ph.D. from Texas Tech University in 1992. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master's student projects, most of which involve a combination of information security and machine learning.
目次
Introduction. I TOOLS OF THE TRADE. A Revealing Introduction to Hidden Markov Models. A Full Frontal View of Profile Hidden Markov Models. Principal Components of Principal. Component Analysis. A Reassuring Introduction to Support Vector Machines. A Comprehensible Collection of Clustering Concepts. Many Mini Topics. Data Analysis. II APPLICATIONS. HMM Applications. PHMM Applications. PCA Applications. SVM Applications. Clustering Applications
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