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Introduction to Machine Learning with Applications in Information Security
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Mark Stamp (Department of Computer Science, San Jose State University)
Introduction to Machine Learning with Applications in Information Security by Mark Stamp (Department of Computer Science, San Jose State University) at Abbey's Bookshop,

Introduction to Machine Learning with Applications in Information Security

Mark Stamp (Department of Computer Science, San Jose State University)


CRC Press

Probability & statistics;
Automatic control engineering;
Computer security;
Machine learning


346 pages

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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: 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.

By:   Mark Stamp (Department of Computer Science San Jose State University)
Imprint:   CRC Press
Country of Publication:   United Kingdom
Dimensions:   Height: 235mm,  Width: 156mm, 
Weight:   658g
ISBN:   9781138626782
ISBN 10:   1138626783
Pages:   346
Publication Date:   September 2017
Audience:   College/higher education ,  College/higher education ,  A / AS level ,  Primary
Format:   Hardback
Publisher's Status:   Active

Introduction What is Machine Learning? ‚ ® About This Book‚ ® Necessary Background A Few Too Many Notes I TOOLS OF THE TRADE A Revealing Introduction to Hidden Markov Models Introduction and Background A Simple Example Notation The Three Problems The Three Solutions Dynamic Programming ‚ ® Scaling‚ ® All Together Now The Bottom Line‚ ® A Full Frontal View of Profile Hidden Markov Models‚ ® Introduction Overview and Notation Pairwise Alignment Multiple Sequence Alignment PHMM from MSA Scoring The Bottom Line Principal Components of Principal Component Analysis Introduction‚ ® Background Principal Component Analysis ‚ ® SVD Basics ‚ ® All Together Now A Numerical Example ‚ ® The Bottom Line‚ ® A Reassuring Introduction to Support Vector Machines Introduction‚ ® Constrained Optimization AC loser Look at SVM All Together Now‚ ® A Note on Quadratic Programming‚ ® The Bottom Line‚ ® Problems ‚ ® A Comprehensible Collection of Clustering Concepts Introduction Overview and Background -Means Measuring Cluster Quality EM Clustering The Bottom Line Problems Many Mini Topics Introduction -Nearest Neighbors Neural Networks Boosting Random Forest Linear Discriminant Analysis VectorQuantization Naive Bayes Regression Analysis Conditional Random Fields Data Analysis Introduction Experimental Design Accuracy ROC Curves Imbalance Problem PR Curves The Bottom Line II APPLICATIONS HMM Applications Introduction English Text Analysis ‚ ® Detecting Undetectable Malware‚ ® Classic Cryptanalysis PHMM Applications Introduction Masquerade Detection Malware Detection PCA Applications Introduction Eigenfaces Eigenviruses Eigenspam SVM Applications Introduction Malware Detection Image Spam Revisited Clustering Applications Introduction -Means for Malware Classification EM vs -Means for Malware Analysis

Mark Stamp is a Professor at San Jose State University, and the author of two textbooks, Information Security: Principles and Practice and Applied Cryptanalysis: Breaking Ciphers in the Real World.

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