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Journals / Acta Infologica / 2024 / Volume: 8 Issue: 2

Advanced Phishing Detection: Leveraging t-SNE Feature Extraction and Machine Learning on a Comprehensive URL Dataset

Taha Etem(Çankırı Karatekin University, Computer Engineer ing Department)0000-0003-1419-5008
Mustafa Teke(Çankırı Karatekin University, Department of Electrical-Electronics Engineering)0000-0002-7262-4918
Pages
—
Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings pave the way for developing more powerful and flexible phishing detection systems that can identify and neutralize emerging threats in real-time scenarios.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Phishing attacks continue to pose a major challenge in today’s digital world; thus, sophisticated detection techniques are required to address constantly changing tactics.
📊
Key Findings & Data:The proposed dataset comprises 134,850 legitimate URLs and 100,945 phishing URLs, providing a robust foundation for analysis.
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Conclusion & Impact:These findings pave the way for developing more powerful and flexible phishing detection systems that can identify and neutralize emerging threats in real-time scenarios.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Machine learning · cybersecurity · feature extraction

Abstract

Phishing attacks continue to pose a major challenge in today’s digital world; thus, sophisticated detection techniques are required to address constantly changing tactics. In this paper, we have proposed an innovative method to identify phishing attempts using the extensive PhiUSIIL dataset. The proposed dataset comprises 134,850 legitimate URLs and 100,945 phishing URLs, providing a robust foundation for analysis. We applied the t-SNE technique for feature extraction, condensing the original 51 features into only 2, while preserving high detection accuracy. We evaluated several machine learning algorithms on both full and reduced datasets, including Logistic Regression, Naive Bayes, k-Nearest Neighbors (kNN), Decision Trees, and Random Forest. The Decision Tree algorithm showed the best performance on the original dataset, achieving 99.7% accuracy. Interestingly, the proposed kNN demonstrated remarkable results on feature-extracted data, achieving 99.2% accuracy. We observed significant improvements in Logistic Regression and Random Forest performance when using the feature-extracted dataset. The proposed method offers substantial benefits in terms of computational efficiency. The feature-extracted dataset requires less processing power; thus, it is well-suited for systems with limited resources. These findings pave the way for developing more powerful and flexible phishing detection systems that can identify and neutralize emerging threats in real-time scenarios.

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Cite This Article

Etem, T., Teke, M. (2024). Advanced Phishing Detection: Leveraging t-SNE Feature Extraction and Machine Learning on a Comprehensive URL Dataset. Acta Infologica, Volume: 8 Issue: 2. https://doi.org/10.26650/acin.1521835

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