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/.
