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Datasets for phishing websites detection.

Grega Vrbančič¹, Iztok Fister¹, Vili Podgorelec¹

¹Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, Maribor SI-2000, Slovenia.

|November 16, 2020

Summary

This summary is machine-generated.

This study introduces two large datasets for detecting phishing websites, crucial for combating online fraud. These resources aid researchers in developing advanced machine learning models for enhanced cybersecurity defenses against phishing attacks.

Keywords:

Classification Computer security Optimization Phishing websites

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Area of Science:

Cybersecurity
Machine Learning
Data Science

Background:

Phishing attacks increasingly use deceptive websites to steal sensitive user data.
Detecting these fraudulent sites is a growing challenge in cybersecurity.
Machine learning approaches are being applied to identify phishing websites.

Purpose of the Study:

To provide researchers with substantial, labeled datasets for training and evaluating phishing detection models.
To facilitate the development of robust phishing detection systems.
To enable association rule mining for understanding phishing patterns.

Main Methods:

Creation of two distinct datasets comprising 58,645 and 88,647 website samples.
Labeling of all websites as either legitimate or phishing.
Preparation of data for machine learning model training and analysis.

Main Results:

Availability of two comprehensive datasets for phishing website research.
Datasets are suitable for training classification models and building detection systems.
Enables further research into association rules for phishing behavior.

Conclusions:

The presented datasets are valuable resources for the machine learning community focused on cybersecurity.
Facilitates advancements in automated phishing detection and prevention.
Supports the development of more effective strategies against online fraud.