Introduction to Machine Learning with Applications in Information Security, Second Edition

Machine learning becomes easier to grasp when its methods are tied to problems they can help address. In this second edition, Mark Stamp introduces a broad range of machine-learning and deep-learning techniques through practical examples, many drawn from information security and malware analysis. The emphasis is on understanding how the ideas work—not on proving theorems or getting lost in mathematical formalism.

Core methods, clearly introduced

The book develops foundational approaches including Hidden Markov Models, Support Vector Machines, clustering, k-Nearest Neighbor, boosting, Random Forests, and Linear Discriminant Analysis. These topics offer readers a foundation for recognizing how different learning methods approach patterns and data.

From neural networks to newer architectures

Coverage extends from backpropagation, multilayer perceptrons, and convolutional and recurrent neural networks to architectures and methods such as LSTMs, GANs, residual networks, deep belief networks, BERT, and Word2Vec. Attention, dropout, explainability, and adversarial attacks broaden the discussion to important developments in deep learning.

Security applications make the ideas concrete

Information security is more than a passing example: it supplies much of the book’s practical context. Applications focus especially on malware, showing how learning techniques can be used in straightforward security scenarios. This connection gives readers a way to consider algorithms alongside the kinds of problems they may be applied to.

An approachable introduction with some programming

Stamp presents the material at an intuitive level. Some exercises involve programming, and a few application sections assume elementary computing knowledge; the book notes that modest computing experience should be enough to follow that work.

For readers exploring machine learning and security

This edition will interest students and curious practitioners who want an introductory survey of machine learning and deep learning with a clear information-security perspective. It brings classic algorithms, modern neural-network approaches, and applied security examples together in one substantial textbook.