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Phishing Detection Using Content-Based Image Classification

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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.

    Author(s): Shekhar Khandelwal, Rik Das
    Publisher: CRC Press/Chapman & Hall
    Year: 2022

    Language: English
    Pages: 131
    City: Boca Raton

    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