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Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 26 Sayı: 1

Stacking-Based Model for Phishing Website Detection System

Kimlik Avı Web Sitesi Algılama Sistemi için Yığın Tabanlı Model

Cihan Bayraktar(KARABÜK ÜNİVERSİTESİ, ESKİPAZAR MESLEK YÜKSEKOKULU, BİLGİSAYAR TEKNOLOJİLERİ BÖLÜMÜ, BİLİŞİM GÜVENLİĞİ TEKNOLOJİSİ PR.)0000-0003-4321-5485
Pages
78–88
Publication Date
2026-01-19
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AI Summary & Key Takeaways

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

Later, a new model created with the stacking method using these 3 models increased the accuracy rate to 99.99%, demonstrating the effectiveness of the study in detecting phishing websites.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Phishing attacks pose a significant threat in cybersecurity and therefore their detection is of critical importance to ensure the security of digital environments.
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Key Findings & Data:The primary objective is to develop high-performance machine learning models to detect phishing websites using PyCaret.
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Conclusion & Impact:Later, a new model created with the stacking method using these 3 models increased the accuracy rate to 99.99%, demonstrating the effectiveness of the study in detecting phishing websites.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kimlik Avı · Makine Öğrenmesi · PyCaret

Abstract

Phishing attacks pose a significant threat in cybersecurity and therefore their detection is of critical importance to ensure the security of digital environments. Machine learning methods have proven to be an effective approach to detect phishing websites. In this study, PyCaret library, a powerful tool among automatic machine learning libraries, is used to implement various machine learning algorithms quickly and efficiently. The primary objective is to develop high-performance machine learning models to detect phishing websites using PyCaret. For this purpose, PhiUSIIL dataset is used. Multiple machine learning algorithms were analyzed on the dataset using PyCaret library, the best performing algorithms were identified and further optimized through hyperparameter tuning. In addition, an ensemble model was created using the stacking method and the performance was improved. The analysis revealed that the Logistic Regression algorithm achieved 99.98% accuracy, the K-Nearest Neighbors algorithm 99.74% accuracy, and the Naive Bayes algorithm 99.24% accuracy. Later, a new model created with the stacking method using these 3 models increased the accuracy rate to 99.99%, demonstrating the effectiveness of the study in detecting phishing websites.

Özet

Kimlik avı saldırıları siber güvenlikte önemli bir tehdit oluşturmaktadır ve bu nedenle tespiti dijital ortamların güvenliğini sağlamak için kritik öneme sahiptir. Makine öğrenimi yöntemlerinin kimlik avı web sitelerini tespit etmek için etkili bir yaklaşım olduğu kanıtlanmıştır. Bu çalışmada, otomatik makine öğrenimi kütüphaneleri arasında güçlü bir araç olan PyCaret kütüphanesi kullanılarak çeşitli makine öğrenimi algoritmaları hızlı ve verimli bir şekilde uygulanmıştır. Birincil amaç, PyCaret kullanarak kimlik avı web sitelerini tespit etmek için yüksek performanslı makine öğrenimi modelleri geliştirmektir. Bu amaçla PhiUSIIL veri seti kullanılmıştır. PyCaret kütüphanesi kullanılarak veri seti üzerinde birden fazla makine öğrenimi algoritması analiz edilmiş, en iyi performans gösteren algoritmalar belirlenmiş ve hiperparametre ayarı yoluyla daha da optimize edilmiştir. Ayrıca, yığınlama yöntemi kullanılarak bir topluluk modeli oluşturulmuş ve performans iyileştirilmiştir. Analiz, Lojistik Regresyon algoritmasının %99,98, K-En Yakın Komşular algoritmasının %99,74 ve Naive Bayes algoritmasının %99,24 doğruluk elde ettiğini ortaya koymuştur. Daha sonra bu 3 model kullanılarak stacking yöntemi ile oluşturulan yeni bir model ile doğruluk oranı %99,99'a çıkarılarak çalışmanın phishing sitelerini tespit etmedeki etkinliği ortaya konuldu.

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Cite This Article

Bayraktar, C. (2026). Stacking-Based Model for Phishing Website Detection System. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 26 Sayı: 1, 78–88. https://doi.org/10.35414/akufemubid.1592728

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