Phishing Detection Using Content-Based Image Classification

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A01=Rik Das
A01=Shekhar Khandelwal
Author_Rik Das
Author_Shekhar Khandelwal
Category=UR
Category=UYQV
Class Imbalance
CNN Architecture
CNN Model
computer vision techniques
Convolution Layers
Cyber
Dense
Dimensionality Reduction Technique
DNS
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
F1 Score
FNR
FPR
Hog Feature
image-based cyber threat detection
Iris
Legitimate Counterpart
Legitimate Websites
Ml Classifier
Multi-class Classification
Numpy Array
OpenCV feature extraction
Performance Assessment
Phishing Attacks
Phishing Detection
Phishing Website
principal component analysis
Privacy
Python Implementation
representation learning
Security
SMOTE oversampling
SOTA
Suspicious Website
TPR
transfer learning methods

Product details

  • ISBN 9781032108537
  • Weight: 460g
  • Dimensions: 138 x 216mm
  • Publication Date: 02 Jun 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

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.

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