Phishing Detection Using Content-Based Image Classification

Phishing Detection using Content Based Image Classification explores Phishing detection using computer vision through CNN, transfer learning and representation learning, utilizing ML & DL classifiers. This book is primarily aimed at researchers, professionals and students in field of Computer Vision and Cyber Security domain.

"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 ComputerVision. 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 newadvancement in the field of computer vision and more advanced algorithms"--