Introduction to Machine Learning with Applications in Information Security by Stamp, Mark;

Short description:

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. 

Long description:

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/.

Table of Contents:

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