04900nam a2200685 i 4500 on1314330186 EBZ 20251029164100.0 m d cr cnu---unuuu 220417t20222022flua ob 001 0 eng 2021060070 EBZ eng EBZ GBC273430 bnb 020565735 Uk 9781003217381 electronic book 1003217389 electronic book 9781000597684 electronic book 1000597687 electronic book 9781000597691 electronic book 1000597695 electronic book 9781032108537 hardcover 9781032265025 paperback 10.1201/9781003217381 doi 9781000597691 Ingram Content Group pcc HV6773.15.P45 K43 2022 MAIN Khandelwal, Shekhar, author. 64226 Phishing detection using content-based image classification / Shekhar Khandelwal and Rik Das. First edition. Boca Raton : CRC Press, Taylor & Francis Group, 2022. ©2022 1 online resource (xii, 119 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier textual sapdv EBSCOhost "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. Phishing Prevention. 64227 Image processing Digital techniques. 64228 Image data mining. 64229 Digital images Classification. 64230 Neural networks (Computer science) 64231 digital imaging. aat 64232 Das, Rik, 1978- author. 64233 has work: PHISHING DETECTION USING CONTENT BASED IMAGE CLASSIFICATION (Text) https://id.oclc.org/worldcat/entity/E39PCXvW8jYhKhPDfmT7cVc9Dq https://id.oclc.org/worldcat/ontology/hasWork Print version: Khandelwal, Shekhar. Phishing detection using content based image classification First edition. Boca Raton : Chapman & Hall/CRC Press, 2022 9781032108537 (DLC) 2021060069 EBSCOhost https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3275752 Askews and Holts Library Services ASKH AH39551245 EBSCOhost EBSC 3275752 92 N$T 55945 55945