Table Of ContentINTRODUCTION TO
MACHINE
LEARNING
with
APPLICATIONS
INFORMATION
in
SECURITY
Mark Stamp
San Jose State University
California
CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742
© 2018 by Taylor & Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, an Informa business
International Standard Book Number-13: 978-1-138-62678-2 (Hardback)
Visit the Taylor & Francis Web site at
http://www.taylorandfrancis.com
and the CRC Press Web site at
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Contents
Preface xiii
1 Introduction 1
1.1 What Is Machine Learning? . . . . . . . . . . . . . . . . . . . 1
1.2 About This Book . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Necessary Background . . . . . . . . . . . . . . . . . . . . . . 4
1.4 A Few Too Many Notes . . . . . . . . . . . . . . . . . . . . . 4
I Tools of the Trade 5
2 A Revealing Introduction to Hidden Markov Models 7
2.1 Introduction and Background . . . . . . . . . . . . . . . . . . 7
2.2 A Simple Example . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 The Three Problems . . . . . . . . . . . . . . . . . . . . . . . 14
2.4.1 HMM Problem 1 . . . . . . . . . . . . . . . . . . . . . 14
2.4.2 HMM Problem 2 . . . . . . . . . . . . . . . . . . . . . 14
2.4.3 HMM Problem 3 . . . . . . . . . . . . . . . . . . . . . 14
2.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5 The Three Solutions . . . . . . . . . . . . . . . . . . . . . . . 15
2.5.1 Solution to HMM Problem 1 . . . . . . . . . . . . . . 15
2.5.2 Solution to HMM Problem 2 . . . . . . . . . . . . . . 16
2.5.3 Solution to HMM Problem 3 . . . . . . . . . . . . . . 17
2.6 Dynamic Programming . . . . . . . . . . . . . . . . . . . . . 20
2.7 Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.8 All Together Now . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.9 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.10 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 A Full Frontal View of Profile Hidden Markov Models 37
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 Overview and Notation . . . . . . . . . . . . . . . . . . . . . 39
3.3 Pairwise Alignment . . . . . . . . . . . . . . . . . . . . . . . . 42
3.4 Multiple Sequence Alignment . . . . . . . . . . . . . . . . . . 46
3.5 PHMM from MSA . . . . . . . . . . . . . . . . . . . . . . . . 50
3.6 Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.7 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.8 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4 Principal Components of Principal Component Analysis 63
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.2.1 A Brief Review of Linear Algebra . . . . . . . . . . . . 64
4.2.2 Geometric View of Eigenvectors . . . . . . . . . . . . 68
4.2.3 Covariance Matrix . . . . . . . . . . . . . . . . . . . . 70
4.3 Principal Component Analysis . . . . . . . . . . . . . . . . . 73
4.4 SVD Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5 All Together Now . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5.1 Training Phase . . . . . . . . . . . . . . . . . . . . . . 80
4.5.2 Scoring Phase . . . . . . . . . . . . . . . . . . . . . . . 82
4.6 A Numerical Example . . . . . . . . . . . . . . . . . . . . . . 83
4.7 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.8 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5 A Reassuring Introduction to Support Vector Machines 95
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.2 Constrained Optimization . . . . . . . . . . . . . . . . . . . . 102
5.2.1 Lagrange Multipliers . . . . . . . . . . . . . . . . . . . 104
5.2.2 Lagrangian Duality . . . . . . . . . . . . . . . . . . . . 108
5.3 A Closer Look at SVM . . . . . . . . . . . . . . . . . . . . . . 110
5.3.1 Training and Scoring . . . . . . . . . . . . . . . . . . . 112
5.3.2 Scoring Revisited . . . . . . . . . . . . . . . . . . . . . 114
5.3.3 Support Vectors . . . . . . . . . . . . . . . . . . . . . 115
5.3.4 Training and Scoring Re-revisited . . . . . . . . . . . . 116
5.3.5 The Kernel Trick . . . . . . . . . . . . . . . . . . . . . 117
5.4 All Together Now . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.5 A Note on Quadratic Programming . . . . . . . . . . . . . . . 121
5.6 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.7 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6 A Comprehensible Collection of Clustering Concepts 133
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
6.2 Overview and Background . . . . . . . . . . . . . . . . . . . . 133
6.3 -Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
6.4 Measuring Cluster Quality . . . . . . . . . . . . . . . . . . . . 141
6.4.1 Internal Validation . . . . . . . . . . . . . . . . . . . . 143
6.4.2 External Validation . . . . . . . . . . . . . . . . . . . 148
6.4.3 Visualizing Clusters . . . . . . . . . . . . . . . . . . . 150
6.5 EM Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.5.1 Maximum Likelihood Estimator . . . . . . . . . . . . 154
6.5.2 An Easy EM Example . . . . . . . . . . . . . . . . . . 155
6.5.3 EM Algorithm . . . . . . . . . . . . . . . . . . . . . . 159
6.5.4 Gaussian Mixture Example . . . . . . . . . . . . . . . 163
6.6 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . 170
6.7 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
7 Many Mini Topics 177
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
7.2 -Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . 177
7.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . 179
7.4 Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
7.4.1 Football Analogy . . . . . . . . . . . . . . . . . . . . . 182
7.4.2 AdaBoost . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.5 Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . 186
7.6 Linear Discriminant Analysis . . . . . . . . . . . . . . . . . . 192
7.7 Vector Quantization . . . . . . . . . . . . . . . . . . . . . . . 202
7.8 Na¨ıve Bayes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
7.9 Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . 205
7.10 Conditional Random Fields . . . . . . . . . . . . . . . . . . . 208
7.10.1 Linear Chain CRF . . . . . . . . . . . . . . . . . . . . 209
7.10.2 Generative vs Discriminative Models . . . . . . . . . . 210
7.10.3 The Bottom Line on CRFs . . . . . . . . . . . . . . . 213
7.11 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
8 Data Analysis 219
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
8.2 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . 220
8.3 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
8.4 ROC Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
8.5 Imbalance Problem . . . . . . . . . . . . . . . . . . . . . . . . 228
8.6 PR Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
8.7 The Bottom Line . . . . . . . . . . . . . . . . . . . . . . . . . 231
8.8 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
II Applications 235
9 HMM Applications 237
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
9.2 English Text Analysis . . . . . . . . . . . . . . . . . . . . . . 237
9.3 Detecting Undetectable Malware . . . . . . . . . . . . . . . . 240
9.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . 240
9.3.2 Signature-Proof Metamorphic Generator . . . . . . . . 242
9.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 243
9.4 Classic Cryptanalysis . . . . . . . . . . . . . . . . . . . . . . . 245
9.4.1 Jakobsen’s Algorithm . . . . . . . . . . . . . . . . . . 245
9.4.2 HMM with Random Restarts . . . . . . . . . . . . . . 251
10 PHMM Applications 261
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
10.2 Masquerade Detection . . . . . . . . . . . . . . . . . . . . . . 261
10.2.1 Experiments with Schonlau Dataset . . . . . . . . . . 262
10.2.2 Simulated Data with Positional Information . . . . . . 265
10.3 Malware Detection . . . . . . . . . . . . . . . . . . . . . . . . 269
10.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . 270
10.3.2 Datasets and Results . . . . . . . . . . . . . . . . . . . 271
11 PCA Applications 277
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
11.2 Eigenfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
11.3 Eigenviruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
11.3.1 Malware Detection Results . . . . . . . . . . . . . . . 280
11.3.2 Compiler Experiments . . . . . . . . . . . . . . . . . . 282
11.4 Eigenspam. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
11.4.1 PCA for Image Spam Detection. . . . . . . . . . . . . 285
11.4.2 Detection Results . . . . . . . . . . . . . . . . . . . . . 285
12 SVM Applications 289
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289
12.2 Malware Detection . . . . . . . . . . . . . . . . . . . . . . . . 289
12.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . 290
12.2.2 Experimental Results . . . . . . . . . . . . . . . . . . 293
12.3 Image Spam Revisited . . . . . . . . . . . . . . . . . . . . . . 296
12.3.1 SVM for Image Spam Detection . . . . . . . . . . . . 298
12.3.2 SVM Experiments . . . . . . . . . . . . . . . . . . . . 300
12.3.3 Improved Dataset . . . . . . . . . . . . . . . . . . . . 304
13 Clustering Applications 307
13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
13.2 -Means for Malware Classification . . . . . . . . . . . . . . 307
13.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . 308
13.2.2 Experiments and Results . . . . . . . . . . . . . . . . 309
13.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 313
13.3 EM vs -Means for Malware Analysis . . . . . . . . . . . . . 314
13.3.1 Experiments and Results . . . . . . . . . . . . . . . . 314
13.3.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 317
Annotated Bibliography 319
Index 338
Preface
“Perhaps it hasn’t one,” Alice ventured to remark.
“Tut, tut, child!” said the Duchess.
“Everything’s got a moral, if only you can find it.”
— Lewis Carroll, Alice in Wonderland
Forthepastseveralyears,I’vebeenteachingaclasson“TopicsinInformation
Security.” Each time I taught this course, I’d sneak in a few more machine
learning topics. For the past couple of years, the class has been turned on
its head, with machine learning being the focus, and information security
only making its appearance in the applications. Unable to find a suitable
textbook, I wrote a manuscript, which slowly evolved into this book.
In my machine learning class, we spend about two weeks on each of the
major topics in this book (HMM, PHMM, PCA, SVM, and clustering). For
each of these topics, about one week is devoted to the technical details in
Part I, and another lecture or two is spent on the corresponding applica-
tions in Part II. The material in Part I is not easy—by including relevant
applications, the material is reinforced, and the pace is more reasonable.
I also spend a week covering the data analysis topics in Chapter 8 and
several of the mini topics in Chapter 7 are covered, based on time constraints
and student interest.1
Machine learning is an ideal subject for substantive projects. In topics
classes, Ialwaysrequireprojects, whichareusuallycompletedbypairsofstu-
dents,althoughindividualprojectsareallowed. Atleastoneweekisallocated
to student presentations of their project results.
A suggested syllabus is given in Table 1. This syllabus should leave time
for tests, project presentations, and selected special topics. Note that the
applications material in Part II is intermixed with the material in Part I.
Also note that the data analysis chapter is covered early, since it’s relevant
to all of the applications in Part II.
1Who am I kidding? Topics are selected based on my interests, not student interest.
xiv PREFACE
Table 1: Suggested syllabus
Chapter Hours Coverage
1. Introduction 1 All
2. Hidden Markov Models 3 All
9. HMM Applications 2 All
8. Data Analysis 3 All
3. Profile Hidden Markov Models 3 All
10. PHMM Applications 2 All
4. Principal Component Analysis 3 All
11. PCA Applications 2 All
5. Support Vector Machines 3 All
12. SVM Applications 3 All
6. Clustering 3 All
13. Clustering Applications 2 All
7. Mini-topics 6 LDA and selected topics
Total 36
My machine learning class is taught at the beginning graduate level. For
an undergraduate class, it might be advisable to slow the pace slightly. Re-
gardless of the level, labs would likely be helpful. However, it’s important to
treat labs as supplemental to—as opposed to a substitute for—lectures.
Learningchallengingtechnicalmaterialrequiresstudyingitmultipletimes
in multiple different ways, and I’d say that the magic number is three. It’s no
accident that students who read the book, attend the lectures, and conscien-
tiouslyworkonhomeworkproblemslearnthismaterialwell. Ifyouaretrying
to learn this subject on your own, the author has posted his lecture videos
online, and these might serve as a (very poor) substitute for live lectures.2
I’m also a big believer in learning by programming—the more code that you
write, the better you will learn machine learning.
Mark Stamp
Los Gatos, California
April, 2017
2In my experience, in-person lectures are infinitely more valuable than any recorded or
online format. Something happens in live classes that will never be fully duplicated in any
dead (or even semi-dead) format.
Chapter 1
Introduction
I took a speed reading course and read War and Peace in twenty minutes.
It involves Russia.
— Woody Allen
1.1 What Is Machine Learning?
For our purposes, we’ll view machine learning as a form of statistical discrim-
ination, where the “machine” does the heavy lifting. That is, the computer
“learns” important information, saving us humans from the hard work of
trying to extract useful information from seemingly inscrutable data.
For the applications considered in this book, we typically train a model,
then use the resulting model to score samples. If the score is sufficiently high,
we classify the sample as being of the same type as was used to train the
model. And thanks to the miracle of machine learning, we don’t have to
work too hard to perform such classification. Since the model parameters are
(more-or-less) automatically extracted from training data, machine learning
algorithms are sometimes said to be data driven.
Machine learning techniques can be successfully applied to a wide range
of important problems, including speech recognition, natural language pro-
cessing, bioinformatics, stock market analysis, information security, and the
homework problems in this book. Additional useful applications of machine
learning seem to be found on a daily basis—the set of potential applications
is virtually unlimited.
It’spossibletotreatanymachinelearningalgorithmasablackboxand,in
fact, thisisamajorsellingpointsofthefield. Manysuccessfulmachinelearn-
ers simply feed data into their favorite machine learning black box, which,
surprisingly often, spits out useful results. While such an approach can work,
