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Journals / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 13 Sayı: 2

Phishing E-mail Detection with Machine Learning and Deep Learning: Improving Classification Performance with Proposed New Features

Hadjer Brioua(ONDOKUZ MAYIS ÜNİVERSİTESİ)0009-0003-9829-1810
Havvanur Siyambaş(ONDOKUZ MAYIS ÜNİVERSİTESİ)0009-0002-3911-4286
Durmuş Özkan Şahin(ONDOKUZ MAYIS ÜNİVERSİTESİ)0000-0002-0831-7825
Pages
183–193
Publication Date
2025-06-30
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AI Summary & Key Takeaways

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

The addition of these features generally improved classification results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Today, with the increasing use of the internet, individuals who use email have become potential targets for fraudsters.
📊
Key Findings & Data:This study proposes a machine learning-based system for detecting phishing attacks using the SeFACED dataset, which was adjusted for binary classification with 12,498 normal and 5,142 fraudulent email data points.
💡
Conclusion & Impact:The addition of these features generally improved classification results.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Phishing · Phishing e-mail · Phishing attacks

Abstract

Today, with the increasing use of the internet, individuals who use email have become potential targets for fraudsters. These malicious groups send fake or misleading emails to steal sensitive information such as identity, bank, and social media credentials. This tactic is known as phishing. This study proposes a machine learning-based system for detecting phishing attacks using the SeFACED dataset, which was adjusted for binary classification with 12,498 normal and 5,142 fraudulent email data points. Python was used for programming, with Google Colab and Jupyter Notebook as development platforms. Email data underwent data collection, cleaning, and word stem separation processes. Three feature extraction techniques were used: Bag of Words, TF-IDF, and Word2Vec. Six algorithms, including Logistic Regression, Random Forest, Support Vector Machines, Naive Bayes, Convolutional Neural Network, and Long Short-Term Memory, were employed for classification. Performance was evaluated using metrics like accuracy, preci-sion, recall, and F1-score. New attributes proposed to enhance detection included CSS tags, HTML tags, black-list words, link errors, and grammar and spelling errors. The addition of these features generally improved classification results.

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Cite This Article

Brioua, H., Siyambaş, H., Şahin, D. Ö. (2025). Phishing E-mail Detection with Machine Learning and Deep Learning: Improving Classification Performance with Proposed New Features. Balkan Journal of Electrical and Computer Engineering, Cilt: 13 Sayı: 2, 183–193. https://doi.org/10.17694/bajece.1490596

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