"A Chapman & Hall book."

Includes bibliographical references and index.

"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"-- Provided by publisher.

EBSCOhost "EBSCO evaluates our products based on the Web Content Accessibility Guidelines (WCAG) and the related Section 508 and EN 301 549 regulations in the US and EU. Most EBSCO products are substantially conformant with WCAG 2.2 level AA." Source: https://connect.ebsco.com/s/article/EBSCO-VPATs?language=en_US. Last accessed April 22, 2025.

Description based on online resource; title from digital title page (viewed on September 23, 2022).

Phishing and cybersecurity -- Image processing based phishing detection techniques -- Implementing CNN for classifying phishing websites -- Transfer learning approach in phishing detection -- Feature extraction and representation learning -- Dimensionality reduction techniques -- Feature fusion techniques -- Comparison of phishing detection approaches -- Basics of digital image processing.