Introduction to machine learning with applications in information security
Mark Stamp.
Bok · Engelsk · 2017
| Omfang | xiv, 345 s. : : ill.
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| Opplysninger | 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.. - 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 Naïve Bayes Regression Analysis Conditional Random Fields.. - 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. •Provides a comprehensive introduction to fundamental machine learning concepts •Emphasizes depth over breadth •Explores information security applications •Presents malware detection, intrusion detection, and cryptography, as applied to machine learning •Authored by a recognized expert in the field.
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| Dewey | |
| ISBN | 9781138626782
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