Phishing Detection Using Content-Based Image Classification

Description

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. 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