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APuML: An Efficient Approach to Detect Mobile Phishing Webpages using Machine Learning.

Ankit Kumar Jain¹, Ninmoy Debnath¹, Arvind Kumar Jain²

¹National Institute of Technology Kurukshetra, Kurukshetra, India.

Wireless Personal Communications

|May 9, 2022

Summary

This summary is machine-generated.

This study introduces Anti Phishing using Machine Learning (APuML) to detect malicious mobile websites. APuML achieves 93.85% accuracy, offering a robust solution against mobile web threats.

Keywords:

Machine learning Malicious mobile webpages Phishing Smartphones

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Design and Analysis for Fall Detection System Simplification

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Area of Science:

Cybersecurity
Machine Learning
Mobile Computing

Background:

Mobile phone usage and web access have surged, increasing vulnerability to sophisticated cyber threats like phishing.
Existing mobile security mechanisms and browser capabilities lag behind desktop solutions, necessitating specialized detection methods.
Current anti-phishing techniques for mobile devices are insufficient, highlighting the need for advanced, comprehensive solutions.

Purpose of the Study:

To develop and present an efficient approach for detecting malicious mobile webpages.
To address the limitations of current mobile anti-phishing strategies.
To provide users with an interactive mobile application for enhanced security.

Main Methods:

The proposed Anti Phishing using Machine Learning (APuML) approach extracts static and site popularity features from URLs.
A feature vector is created and analyzed using machine learning classification algorithms.
The Random Forest classifier was selected for its superior performance in identifying malicious sites.

Main Results:

The APuML approach, utilizing the Random Forest classifier, achieved a high detection accuracy of 93.85%.
The system demonstrated effectiveness in identifying various advanced threats, including drive-by downloads, zero-day attacks, and clickjacking.
An endpoint application was developed for seamless user interaction on mobile devices.

Conclusions:

APuML offers an efficient and accurate method for detecting malicious mobile webpages, significantly improving mobile security.
The approach successfully identifies a range of sophisticated attacks, providing a more comprehensive defense than existing methods.
The developed mobile application enhances user engagement and accessibility for real-time threat detection.