Introduction to Machine Learning with Applications in Information Security
(Englisch)
Mark Stamp (Department of Computer Science, San Jose State University)

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. He previously worked at the National Security Agency (NSA) for seven years, which was followed by two years at a small Silicon Valley startup company.

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 learning 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, including 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 malware. 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 computing 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/.

1. Introduction
2. Introduction to Probability Sampling
3. Simple Random Sampling
4. Stratified Simple Random Sampling
5. Systematic Random Sampling
6. Cluster Random Sampling
7. Two-Stage Cluster Random Sampling
8. Sampling with Probabilities Proportional to Size
9. Balanced and Well-Spread Sampling
10. Model-Assisted Estimation
11. Two-Phase Random Sampling
12. Computing the Required Sample Size
13. Model-Based Optimisation of Probability Sampling Designs
14. Sampling for Estimating Parameters of Domains
15. Repeated Sample Surveys for Monitoring Population Parameters
16. Introduction to Sampling for Mapping
17. Regular Grid and Spatial Coverage Sampling
18. Covariate Space Coverage Sampling
19. Conditioned Latin Hypercube Sampling
20. Spatial Response Surface Sampling
21. Introduction to Kriging
22. Model-Based Optimisation of the Grid Spacing
23. Model-Based Optimisation of the Sampling Pattern
24. Sampling for Estimating the SemiVariogram
25. Sampling for Validation of Maps
26. Design-Based, Model-Based and Model-Assisted Approach for Sampling and Inference

1. Preface

   About the Author

   1. What is Machine Learning?
   2. A Revealing Introduction to Hidden Markov Models
   3. Principles of Principal Component Analysis
   4. A Reassuring Introduction to Support Vector Machines
   5. A Comprehensible Collection of Clustering Concepts
   6. Many Mini Topics
   7. Deep Thoughts on Deep Learning
   8. Onward to Backpropagation
   9. A Deeper Diver into Deep Learning
   10. Alphabet Soup of Deep Learning Topics
   11. HMMs for Classic Cryptanalysis
   12. Image Spam Detection
   13. Image-Based Malware Analysis
   14. Malware Evolution Detection
   15. Experimental Design and Analysis
   16. Epilogue

   References

   Index

Inhaltsverzeichnis

1. Preface

   About the Author

   1. What is Machine Learning?
   2. A Revealing Introduction to Hidden Markov Models
   3. Principles of Principal Component Analysis
   4. A Reassuring Introduction to Support Vector Machines
   5. A Comprehensible Collection of Clustering Concepts
   6. Many Mini Topics
   7. Deep Thoughts on Deep Learning
   8. Onward to Backpropagation
   9. A Deeper Diver into Deep Learning
   10. Alphabet Soup of Deep Learning Topics
   11. HMMs for Classic Cryptanalysis
   12. Image Spam Detection
   13. Image-Based Malware Analysis
   14. Malware Evolution Detection
   15. Experimental Design and Analysis
   16. Epilogue

   References

   Index

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   Klappentext

   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.