Phishing Detection Using Content-Based Image Classification

✍ Scribed by Shekhar Khandelwal, Rik Das

• Publisher: CRC Press/Chapman & Hall
• Year: 2022
• Tongue: English
• Leaves: 133
• Category: Library

✦ Synopsis

Phishing Detection Using Content-Based Image Classification is an invaluable resource for any deep learning and cybersecurity professional and scholar trying to solve various cybersecurity tasks using new age technologies like Deep Learning and Computer Vision. With various rule-based phishing detection techniques at play which can be bypassed by phishers, this book provides a step-by-step approach to solve this problem using Computer Vision and Deep Learning techniques with significant accuracy.

The book offers comprehensive coverage of the most essential topics, including:

• Programmatically reading and manipulating image data

• Extracting relevant features from images

• Building statistical models using image features

• Using state-of-the-art Deep Learning models for feature extraction

• Build a robust phishing detection tool even with less data

• Dimensionality reduction techniques

• Class imbalance treatment

• Feature Fusion techniques

• Building performance metrics for multi-class classification task

Another unique aspect of this book is it comes with a completely reproducible code base developed by the author and shared via python notebooks for quick launch and running capabilities. They can be leveraged for further enhancing the provided models using new advancement in the field of computer vision and more advanced algorithms.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Authors
Chapter 1 Phishing and Cybersecurity
Structure
Objective
Basics of Phishing in Cybersecurity
Phishing Detection Techniques
List (Whitelist/Blacklist)-Based
Heuristics (Pre-Defined Rules)-Based
Visual Similarity-Based
Race between Phishers and Anti-Phishers
Chapter 2 Image Processing-Based Phishing Detection Techniques
Structure
Objective
Image Processing-Based Phishing Detection Techniques
Comparison-Based Techniques
Machine Learning-Based Techniques
Challenges in Phishing Detection Using Website Images
Comparison of Techniques
Summary of Phishing Detection Using Image Processing Techniques
Various Experimentations Using CNN
Summary
Chapter 3 Implementing CNN for Classifying Phishing Websites
Structure
Objective
Data Selection and Pre-Processing
Classification Using CNN
CNN Implementation
Label Encoding for Machine Learning Classifier
One Hot Encoding for Deep Learning Classifier
Performance Metrics
Building a Convolutional Neural Network Model
Summary
Chapter 4 Transfer Learning Approach in Phishing Detection
Structure
Objective
Classification Using Transfer Learning
Transfer the Learning up to the Last Fully Connected Layer
Transfer Learning Implementation in Python
Performance Assessment of the CNN Models
Summary
Chapter 5 Feature Extraction and Representation Learning
Structure
Objective
Classification Using Representation Learning
Data Preparation
Convert Images to Numpy Arrays
Feature Extraction Using CNN Off-the-Shelf Architectures
Xception
VGG19
ResNet50
InceptionV3
InceptionResNetV2
MobileNet
DenseNet121
Handling Class Imbalance
Adding Synthetic Data Using SMOTE
SMOTE Python Implementation
Machine Learning Classifier
Performance Assessment of Various Experimentations
Summary
Chapter 6 Dimensionality Reduction Techniques
Structure
Objective
Dimensionality Reduction Using PCA
PCA Python Implementation
Performance Assessment of Various Experimentations
Summary
Chapter 7 Feature Fusion Techniques
Structure
Objective
Basics of Feature Fusion Technique
Different Combinations of Image Representations
Different Feature Fusion Approaches
Fusing Features Horizontally Extracted from the Last Convolution Layer and after Treating Class Imbalance in a Combination of Two CNN Models
Fusing Features Horizontally Extracted from the Last Convolution Layer and after Treating Class Imbalance in a Combination of Three CNN Models
Fusing PCA’d Features Horizontally Extracted from the Last Convolution Layer and Class Imbalance Treatment in a Combination of Two CNN Models
Fusing PCA’d Features Horizontally Extracted from the Last Convolution Layer and Class Imbalance Treatment in a Combination of Three CNN Models
Fusing PCA’d Features Vertically Extracted from the Last Convolution Layer and Class Imbalance Treatment in a Combination of Two CNN Models
Fusing PCA’d Features Vertically Extracted from the Last Convolution Layer and Class Imbalance Treatment in a Combination of Three CNN Models
Performance Assessment of Various Experimentations
Summary
Chapter 8 Comparison of Phishing Detection Approaches
Classification Approaches
Evaluation of Classification Experiments
Comparison of the Best Performing Model with the State-of-the-Art Technique
Summary
Chapter 9 Basics of Digital Image Processing
Structure
Objective
Basics of Digital Image Processing
What Is a Digital Image?
Loading and Displaying Images
References
Index