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IPDM: An Intelligent Phishing Detection Model for E-Commerce Websites

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Vipin Kumar
Kakali Chatterjee
Published in
Data Science and Network Engineering
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the critical issue of phishing attacks on e-commerce websites, focusing on the use of machine learning models to detect and prevent these threats. The authors compare the effectiveness of various classifiers, including K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), in identifying phishing URLs. The study highlights the superior performance of CNN models, achieving an impressive accuracy rate of 99.80%. The research also addresses the challenges of data imbalance and feature engineering, employing techniques like SMOTE and SHAP values to enhance model accuracy and interpretability. The authors discuss the practical applications of these models, including real-time implementation as browser extensions or email scanners. The chapter concludes with a discussion on future enhancements, such as adaptive learning and visual phishing detection, to continuously improve the detection of evolving phishing techniques.
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previous chapter A Novel Hybrid Scheduling Approach for Enhancing Cloud System Performance
next chapter Unsupervised Pattern Discovery in Cyber Incidents Using Principal Component Analysis K-Means DBSCAN and Isolation Forest
Title
IPDM: An Intelligent Phishing Detection Model for E-Commerce Websites
Authors
Vipin Kumar
Kakali Chatterjee
Copyright Year
2026
Book
Data Science and Network Engineering

Print ISBN: 978-3-032-07734-9

Electronic ISBN: 978-3-032-07735-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-07735-6_26
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