Beginning Anomaly Detection Using Python-Based Deep Learning [2 ed.] 9798868800085, 9798868800078

Table of contents :
Table of Contents
About the Authors
About the Technical Reviewers
Acknowledgments
Introduction
Chapter 1: Introduction to Anomaly Detection
What Is an Anomaly?
Anomalous Swans
Anomalies as Data Points
Anomalies in a Time Series
Personal Spending Pattern
Taxi Cabs
Categories of Anomalies
Data Point–Based Anomalies
Context-Based Anomalies
Pattern-Based Anomalies
Anomaly Detection
Outlier Detection
Noise Removal
Novelty Detection
Event Detection
Change Point Detection
Anomaly Score Calculation
The Three Styles of Anomaly Detection
Where Is Anomaly Detection Used?
Data Breaches
Identity Theft
Manufacturing
Networking
Medicine
Video Surveillance
Environment
Summary
Chapter 2: Introduction to Data Science
Data Science
Dataset
Pandas, Scikit-Learn, and Matplotlib
Data I/O
Data Loading
Data Saving
DataFrame Creation
Data Manipulation
Select
Filtering
Sorting
Applying Functions
Grouping
Combining DataFrames
Creating, Renaming, and Dropping Columns
Data Analysis
Value Counts
Pandas .describe() Method
Pandas Correlation Matrix
Visualization
Line Chart
Chart Customization
Scatter Plot
Histogram
Bar Graph
Data Processing
Nulls
Categorical Encoding
Scaling and Normalizing
Feature Engineering and Selection
Summary
Chapter 3: Introduction to Machine Learning
Machine Learning
Introduction to Machine Learning
Data Splitting
Modeling and Evaluation
Classification Metrics
Regression Metrics
Overfitting and Bias-Variance Tradeoff
Hyperparameter Tuning
Validation
Summary
Chapter 4: Traditional Machine Learning Algorithms
Traditional Machine Learning Algorithms
Isolation Forest
Example of an Isolation Forest
Anomaly Detection with an Isolation Forest
Data Preparation
Training
Hyperparameter Tuning
Evaluation and Summary
One-Class Support Vector Machine
How Does OC-SVM Work?
Anomaly Detection with OC-SVM
Data Preparation
Training
Hyperparameter Tuning
Evaluation and Summary
Summary
Chapter 5: Introduction to Deep Learning
Introduction to Deep Learning
What Is Deep Learning?
The Neuron
Activation Functions
Neural Networks
Loss Functions
Regression
Classification
Gradient Descent and Backpropagation
Loss Curve
Regularization
Optimizers
Multilayer Perceptron Supervised Anomaly Detection
Simple Neural Network: Keras
Simple Neural Network: PyTorch
Summary
Chapter 6: Autoencoders
What Are Autoencoders?
Simple Autoencoders
Sparse Autoencoders
Deep Autoencoders
Convolutional Autoencoders
Denoising Autoencoders
Variational Autoencoders
Summary
Chapter 7: Generative Adversarial Networks
What Is a Generative Adversarial Network?
Generative Adversarial Network Architecture
Wasserstein GAN
WGAN-GP
Anomaly Detection with a GAN
Summary
Chapter 8: Long Short-Term Memory Models
Sequences and Time Series Analysis
What Is an RNN?
What Is an LSTM?
LSTM for Anomaly Detection
Examples of Time Series
art_daily_no_noise.csv
art_daily_nojump.csv
art_daily_jumpsdown.csv
art_daily_perfect_square_wave.csv
art_load_balancer_spikes.csv
ambient_temperature_system_failure.csv
ec2_cpu_utilization.csv
rds_cpu_utilization.csv
Summary
Chapter 9: Temporal Convolutional Networks
What Is a Temporal Convolutional Network?
Dilated Temporal Convolutional Network
Anomaly Detection with the Dilated TCN
Encoder-Decoder Temporal Convolutional Network
Anomaly Detection with the ED-TCN
Summary
Chapter 10: Transformers
What Is a Transformer?
Transformer Architecture
Transformer Encoder
Transformer Decoder
Transformer Inference
Anomaly Detection with the Transformer
Summary
Chapter 11: Practical Use Cases and Future Trends of Anomaly Detection
Anomaly Detection
Real-World Use Cases of Anomaly Detection
Telecom
Banking
Environmental
Health Care
Transportation
Social Media
Finance and Insurance
Cybersecurity
Video Surveillance
Manufacturing
Smart Home
Retail
Implementation of Deep Learning–Based Anomaly Detection
Future Trends
Summary
Index