A Novel Ensemble Learning-Based Machine Learning Model for Phishing Attack

Abstract

In today's world, the internet is increasingly effective in every aspect of our lives. The internet, which provides countless advantages when used consciously, also carries many dangers in its other aspect. One of these dangers and the most important one is the possibility of being targeted by malicious people while using the internet. Attackers can deceive innocent people by directing them to fake, misleading websites to obtain our important information and data. With this type of attack, known as phishing attack, internet users can provide their information and data to attackers. In this study, we propose a new ensemble learning-based machine learning model with feature selection methods to detect phishing attacks. We also try two feature selection algorithms to increase the classification success of the model and analyze the effects of these algorithms on the classification success. After the feature selection algorithms, the dataset with the selected features was trained with a new ensemble learning model that we created with the voting classifier method using XGBoost, CatBoost, LightGBM algorithms. The proposed model was analyzed using widely used performance evaluation metrics, achieving an accuracy of 97.96%. It was observed that the proposed model outperforms the studies in the literature using the same dataset.

Keywords

• Phishing Attack
• Ensemble Learning model
• Machine Learning

KİMLİK AVI SALDIRISI İÇİN YENİ BİR TOPLULUK ÖĞRENME MODELİ TABANLI MAKİNE ÖĞRENMESİ MODELİ

Abstract

Günümüzde internet hayatımızın her alanında etkinliğini giderek artırmaktadır. Bilinçli kullanıldığında sayısız avantaj sağlayan internet, diğer yönüyle de birçok tehlikeleri bünyesinde taşımaktadır. Bu tehlikelerden birisi ve en önemlisi interneti kullanırken kötü niyetli kişilerin hedefi olma olasılığıdır. Saldırganlar önemli bilgilerimizi, verilerimizi elde etmek için sahte, yanıltıcı web sitelerine yönlendirerek masum insanları kandırabilmektedir. Kimlik avı saldırısı olarak bilinen bu saldırı tipi ile internet kullanıcıları bilgilerini, verilerini saldırganlara sunabilmektedir. Bu çalışmada, kimlik avı saldırılarını tespit etmek için öznitelik seçimi yöntemlerini de kullanarak yeni bir topluluk öğrenme tabanlı makine öğrenmesi modeli öneriyoruz. Ayrıca modelin sınıflandırma başarısını artırmak için iki öznitelik seçim algoritması deniyoruz ve bu algoritmaların sınıflandırma başarısı üzerindeki etkilerini analiz ediyoruz. Öznitelik seçim algoritmalarından sonra seçilen özniteliklere sahip veriseti ile XGBoost, CatBoost, LightGBM algoritmaları oylama sınıflayıcısı metodu ile oluşturduğumuz yeni bir topluluk öğrenme modeli ile eğitilmiştir. Önerilen model, yaygın olarak kullanılan model performansı değerlendirme metrikleri kullanılarak analiz edilmiştir. Önerilen modelin aynı veri setini kullanan literatürde bulunan çalışmalardan daha üstün performans gösterdiği gözlemlenmiştir.

Keywords

• Kimlik Avı Saldırısı
• Topluluk Öğrenme
• Makine Öğrenmesi

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