Librería Portfolio

Features

Provides a comprehensive introduction to fundamental machine learning concepts
Emphasizes depth over breadth
Explores information security applications
Presents malware detection, intrusion detection, and cryptography, as applied to machine learning
Authored by a recognized expert in the field
Summary

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn't prove theorems, or otherwise 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 machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis.

Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book.



Table of Contents

Introduction

What is Machine Learning? ?

About This Book?

Necessary Background

A Few Too Many Notes

I TOOLS OF THE TRADE

A Revealing Introduction to Hidden Markov Models

Introduction and Background

A Simple Example

Notation

The Three Problems

The Three Solutions

Dynamic Programming ?

Scaling?

All Together Now

The Bottom Line?

A Full Frontal View of Profile Hidden Markov Models?

Introduction

Overview and Notation

Pairwise Alignment

Multiple Sequence Alignment

PHMM from MSA

Scoring

The Bottom Line

Principal Components of Principal Component Analysis

Introduction?

Background

Principal Component Analysis ?

SVD Basics ?

All Together Now

A Numerical Example ?

The Bottom Line?

A Reassuring Introduction to Support Vector Machines

Introduction?

Constrained Optimization

AC loser Look at SVM

All Together Now?

A Note on Quadratic Programming?

The Bottom Line?

Problems ?

A Comprehensible Collection of Clustering Concepts

Introduction

Overview and Background

??-Means

Measuring Cluster Quality

EM Clustering

The Bottom Line

Problems

Many Mini Topics

Introduction

??-Nearest Neighbors

Neural Networks

Boosting

Random Forest

Linear Discriminant Analysis

VectorQuantization

Naïve Bayes

Regression Analysis

Conditional Random Fields

Data Analysis

Introduction

Experimental Design

Accuracy

ROC Curves

Imbalance Problem

PR Curves

The Bottom Line

II APPLICATIONS

HMM Applications

Introduction

English Text Analysis ?

Detecting ´Undetectable´ Malware?

Classic Cryptanalysis

PHMM Applications

Introduction

Masquerade Detection

Malware Detection

PCA Applications

Introduction

Eigenfaces

Eigenviruses

Eigenspam

SVM Applications

Introduction

Malware Detection

Image Spam Revisited

Clustering Applications

Introduction

??-Means for Malware Classification

EM vs ??-Means for Malware Analysis