Introduction to Machine Learning with Applications in Information Security

498 Pages·2022·10.2 MB·English

by  Mark Stamp

Introduction to
Machine Learning
with Applications in
Information Security
Introduction to Machine Learning with Applications in Information Security,
Second Edition provides a classroom-tested introduction to a wide variety of
machine learning and deep learning algorithms and techniques, reinforced via
realistic applications. The book is accessible and doesn’t prove theorems, or 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 classic machine learning topics in depth, including Hidden
Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional
machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random
Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learn-
ing topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer
Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth.
A broad range of advanced deep learning architectures are also presented, includ-
ing Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN),
Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief
Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT),
and Word2Vec. Finally, several cutting-edge deep learning topics are discussed,
including dropout regularization, attention, explainability, and adversarial attacks.
Most of the examples in the book are drawn from the field of information security,
with many of the machine learning and deep learning applications focused on mal-
ware. The applications presented serve to demystify the topics by illustrating the use
of various learning techniques in straightforward scenarios. Some of the exercises in
this book require programming, and elementary computing concepts are assumed
in a few of the application sections. However, anyone with a modest amount of com-
puting 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/.
Chapman & Hall/CRC Machine Learning &
Pattern Recognition
A First Course in Machine Learning
Simon Rogers, Mark Girolami
Statistical Reinforcement Learning: Modern Machine Learning Approaches
Masashi Sugiyama
Sparse Modeling: Theory, Algorithms, and Applications
Irina Rish, Genady Grabarnik
Computational Trust Models and Machine Learning
Xin Liu, Anwitaman Datta, Ee-Peng Lim
Regularization, Optimization, Kernels, and Support Vector Machines
Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou
Machine Learning: An Algorithmic Perspective, Second Edition
Stephen Marsland
Bayesian Programming
Pierre Bessiere, Emmanuel Mazer, Juan Manuel Ahuactzin, Kamel Mekhnacha
Multilinear Subspace Learning: Dimensionality Reduction of
Multidimensional Data
Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos
Data Science and Machine Learning: Mathematical and Statistical Methods
Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman
Deep Learning and Linguistic Representation
Shalom Lappin
Artificial Intelligence and Causal Inference
Momiao Xiong
Introduction to Machine Learning with Applications in Information
Security, Second Edition
Mark Stamp
Entropy Randomization in Machine Learning
Yuri S. Popkov, Alexey Yu. Popkov, Yuri A. Dubno
For more information on this series please visit: https://www.routledge.com/Chapman--
HallCRC-Machine-Learning--Pattern-Recognition/book-series/CRCMACLEAPAT
Introduction to
Machine Learning
with Applications in
Information Security
Second Edition
Mark Stamp
Second edition published 2023
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
CRC Press is an imprint of Taylor & Francis Group, LLC
© 2023 Mark Stamp
First edition published by CRC Press 2017
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ISBN: 978-1-032-20492-5 (hbk)
ISBN: 978-1-032-20717-9 (pbk)
ISBN: 978-1-003-26487-3 (ebk)
DOI: 10.1201/9781003264873
Typeset in Latin Modern font
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Publisher’s note: This book has been prepared from camera-ready copy provided by the authors.
Access the Support Material: http://www.cs.sjsu.edu/~stamp/ML/
To Melody, Austin, and Miles.

Contents
Preface xv
About the Author xix
Acknowledgments xxi
1 What is Machine Learning? 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 About This Book . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Necessary Background . . . . . . . . . . . . . . . . . . . 3
1.4 A Note on Terminology . . . . . . . . . . . . . . . . . . 4
1.5 A Few Too Many Notes . . . . . . . . . . . . . . . . . . 5
I Classic Machine Learning 7
2 A Revealing Introduction to Hidden Markov Models 9
2.1 Introduction and Background . . . . . . . . . . . . . . . 9
2.2 Tree Rings and Temperature . . . . . . . . . . . . . . . 11
2.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 The Three Problems . . . . . . . . . . . . . . . . . . . . 17
2.5 The Three Solutions . . . . . . . . . . . . . . . . . . . . 18
2.5.1 Scoring . . . . . . . . . . . . . . . . . . . . . . . 18
2.5.2 Uncovering Hidden States . . . . . . . . . . . . . 20
2.5.3 Training . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 Dynamic Programming . . . . . . . . . . . . . . . . . . 23
2.7 HMM Scaling . . . . . . . . . . . . . . . . . . . . . . . . 26
2.8 All Together Now . . . . . . . . . . . . . . . . . . . . . . 28
2.9 English Text Example . . . . . . . . . . . . . . . . . . . 32
vii
viii CONTENTS
2.10 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . 36
2.11 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3 Principles of Principal Component Analysis 45
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2.1 A Brief Review of Linear Algebra . . . . . . . . . 47
3.2.2 Geometric View of Eigenvectors . . . . . . . . . 51
3.2.3 Covariance Matrix . . . . . . . . . . . . . . . . . 53
3.3 Principal Component Analysis . . . . . . . . . . . . . . 56
3.4 SVD Basics . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.5 All Together Now . . . . . . . . . . . . . . . . . . . . . . 63
3.5.1 Training Phase . . . . . . . . . . . . . . . . . . . 63
3.5.2 Scoring Phase . . . . . . . . . . . . . . . . . . . . 65
3.6 A Numerical Example . . . . . . . . . . . . . . . . . . . 67
3.7 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . 70
3.8 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4 A Reassuring Introduction to Support Vector
Machines 81
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2 Constrained Optimization . . . . . . . . . . . . . . . . . 89
4.2.1 Lagrange Multipliers . . . . . . . . . . . . . . . . 91
4.2.2 Lagrangian Duality . . . . . . . . . . . . . . . . . 96
4.3 A Closer Look at SVM . . . . . . . . . . . . . . . . . . . 98
4.3.1 Training and Scoring . . . . . . . . . . . . . . . . 100
4.3.2 Scoring Revisited . . . . . . . . . . . . . . . . . . 103
4.3.3 Support Vectors . . . . . . . . . . . . . . . . . . 103
4.3.4 Training and Scoring Re-revisited . . . . . . . . . 104
4.3.5 The Kernel Trick . . . . . . . . . . . . . . . . . . 106
4.4 All Together Now . . . . . . . . . . . . . . . . . . . . . . 109
4.5 A Note on Quadratic Programming . . . . . . . . . . . . 110
4.6 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . 114
4.7 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5 A Comprehensible Collection of Clustering
Concepts 123
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.2 Overview and Background . . . . . . . . . . . . . . . . . 124
5.3 𝐾-Means . . . . . . . . . . . . . . . . . . . . . . . . . . 126
CONTENTS ix
5.4 Measuring Cluster Quality . . . . . . . . . . . . . . . . . 131
5.4.1 Internal Validation . . . . . . . . . . . . . . . . . 133
5.4.2 External Validation . . . . . . . . . . . . . . . . 140
5.4.3 Visualizing Clusters . . . . . . . . . . . . . . . . 141
5.5 EM Clustering . . . . . . . . . . . . . . . . . . . . . . . 144
5.5.1 Maximum Likelihood Estimator . . . . . . . . . 146
5.5.2 An Elementary EM Example . . . . . . . . . . . 147
5.5.3 EM Algorithm . . . . . . . . . . . . . . . . . . . 151
5.5.4 Gaussian Mixture Example . . . . . . . . . . . . 156
5.6 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . 163
5.7 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 164
6 Many Mini Topics 171
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 171
6.2 𝑘-Nearest Neighbors . . . . . . . . . . . . . . . . . . . . 171
6.3 Boost Your Knowledge of Boosting . . . . . . . . . . . . 174
6.3.1 Football Analogy . . . . . . . . . . . . . . . . . . 174
6.3.2 AdaBoost . . . . . . . . . . . . . . . . . . . . . . 175
6.3.3 Examples . . . . . . . . . . . . . . . . . . . . . . 179
6.4 Random Forest . . . . . . . . . . . . . . . . . . . . . . . 185
6.5 Linear Discriminant Analysis . . . . . . . . . . . . . . . 191
6.5.1 LDA Training . . . . . . . . . . . . . . . . . . . . 192
6.5.2 Numerical Example . . . . . . . . . . . . . . . . 199
6.6 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . 202
6.7 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 202
II Deep Learning 207
7 Deep Thoughts on Deep Learning 209
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 209
7.2 A Brief History of Neural Networks . . . . . . . . . . . . 210
7.2.1 McCulloch-Pitts Neuron . . . . . . . . . . . . . . 210
7.2.2 Perceptron . . . . . . . . . . . . . . . . . . . . . 211
7.2.3 Multilayer Perceptron . . . . . . . . . . . . . . . 212
7.2.4 AI Winters and AI Summers . . . . . . . . . . . 214
7.3 Why Deep Learning? . . . . . . . . . . . . . . . . . . . . 215
7.4 Decisions, Decisions . . . . . . . . . . . . . . . . . . . . 216
7.5 Basic Deep Learning Architectures . . . . . . . . . . . . 219
7.5.1 Feedforward Neural Networks . . . . . . . . . . . 219