Phishing Detection Using Content-Based Image Classification
Language: English
Published by Chapman And Hall/CRC, 2024
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
Seller Inventory # 9781032265025
- Title
- Phishing Detection Using Content-Based Image Classification
- Author
- Shekhar Khandelwal
- Publisher
- Chapman And Hall/CRC
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1032265027
- ISBN 13
- 9781032265025
- Item weight
- 179 grams
- Dimensions
- 216x140x8 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
Shekhar Khandelwal is a Data Scientist and works for Ernst & Young (EY) for Data & Analytics team. He has an extensive experience of around 15 years in the industry, and has worked across every sphere of Software Development Lifecycle. He has worked as a product developer, industry solutions developer, data engineer, data scientist and also as a Cloud developer. Previously, he worked for IBM Software labs where he also got a chance to work for industrial IoT based IBM cognitive product development and client deployment using various Watson tools and technologies. He is an industry leader solving challenging Computer Vision, NLP and Predictive Analytics based problems using Machine Learning and Deep Learning.
Dr. Rik Das is currently a Lead Software Engineer in Computer Vision Research at Siemens Advanta, India. Previously he was with Xavier Institute of Social Service, Ranchi, as an Assistant Professor for the Post Graduate Program in Information Technology. Dr.Das has over 17 years of experience in industrial and academic research. He was professionally associated with many leading universities and institutes in India, including Narsee Monjee Institute of Management Studies (NMIMS) (deemed-to-be-university), Globsyn Business School and Maulana Abul Kalam Azad University of Technology. Dr. Das has a Ph.D. (Tech.) in Information Technology from the University of Calcutta. He has also received his M.Tech. (Information Technology) from the University of Calcutta after his B.E. (Information Technology) from the University of Burdwan, West Bengal, India.
"About the title" may belong to another edition of this title.
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