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Related Experiment Videos

Textual and visual content-based anti-phishing: a Bayesian approach.

Haijun Zhang¹, Gang Liu, Tommy W S Chow

¹Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. aarhzhang@gmail.com

IEEE Transactions on Neural Networks

|August 10, 2011

Summary

This summary is machine-generated.

This study introduces a Bayesian framework for detecting phishing web pages using text and visual content. The novel approach effectively identifies malicious sites by fusing classifier results, outperforming individual methods.

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Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Phishing attacks pose a significant threat to internet users, necessitating advanced detection methods.
Existing content-based detection systems often struggle with the dynamic nature of phishing web pages.

Purpose of the Study:

To develop a novel Bayesian framework for content-based phishing web page detection.
To integrate textual and visual content analysis for improved detection accuracy.
To introduce a Bayesian model for estimating classification thresholds.

Main Methods:

A text classifier using the Naive Bayes rule to calculate phishing probabilities.
An image classifier employing Earth Mover's Distance for visual similarity.
A data fusion algorithm utilizing Bayes theory to combine text and image classifier outputs.
A Bayesian model for adaptive threshold estimation in classification.

Main Results:

Individual text and image classifiers demonstrated promising performance.
The proposed data fusion algorithm significantly outperformed individual classifiers.
The Bayesian model proved effective in estimating adaptive matching thresholds.
The framework showed adaptability to various phishing attack scenarios.

Conclusions:

The novel Bayesian framework offers a robust and adaptable solution for content-based phishing web page detection.
Integrating textual and visual analysis with Bayesian methods enhances detection efficacy.
The adaptive threshold estimation is a key innovation for improving classifier performance.