Kimlik Avı Saldırısı için Ensemble Öğrenmesine Dayalı Yeni Bir Model

Abstract

İnternet ortamının hızının artması ve kullanılan altyapıların gelişmesiyle birlikte, insanlar çoğu işlerini çevrimiçi olarak gerçekleştirmeye başlamıştır. Bu durum hayatı kolaylaştırırken, kötü niyetli kişiler tarafından saldırıya maruz kalma olasılığını artırmaktadır. Bu saldırılardan biri de kimlik avıdır. Kimlik avı saldırısında saldırganlar, kopyalanmış, sahte web siteleri oluşturarak kullanıcılardan bilgi çalmayı amaçlamaktadır. Bu saldırı nispeten eski ve kolay olmasına rağmen, düşük bilgi teknolojileri okuryazarlığı nedeniyle hâlâ etkili olabilmektedir. Kullanıcılar, bu sahte web sitelerine anlık tepki, bilgisizlik veya iyi niyetle bilgilerini girebilmekte ve kimlik avı saldırılarına maruz kalabilmektedir. Bir kullanıcının hesap bilgilerinin tehlikeye girmesi, bağlı olduğu kuruluşun veya kurumun güvenliğini de riske atmaktadır. Bu çalışmada, kimlik avı saldırılarını tespit etmek için yeni bir makine öğrenimi tabanlı topluluk (ensemble) model öneriyoruz. Ayrıca, farklı özellik seçimi yöntemlerinin etkisini ölçmek için bir ablasyon çalışmaları sunuyoruz. NaiveStackingSymmetric (NSS) olarak adlandırdığımız model doğruluk (ACC), eğri altındaki alan (AUC) ve F-skor metrikleri ile çokgen alan metriği (PAM) kullanılarak analiz edilmekte ve aynı veri kümesini kullanan diğer çalışmalara göre daha iyi sonuçlara sahip olduğu gösterilmektedir.

Keywords

• Kimlik avı saldırısı
• ensemble öğrenme
• kötücül URL
• stacking
• bilgi güvenliği

A Novel Model Based on Ensemble Learning for Phishing Attack

Abstract

With the increase in the speed of the internet environment and the development of the infrastructures used, people have started to perform most of their work online. As much as this makes life easier, it also increases the possibility of being attacked by malicious people. Attackers can activate a phishing attack that aims to steal information from victims by creating copied, fake websites. While this attack is very old and somewhat simple, it can still be effective due to low IT literacy. People can enter their information on these fake websites out of spontaneity or ignorance or good intentions and be exposed to Phishing attacks. The compromise of a user's account information also puts at risk the security of the organization or institution to which it is connected. In this study, we propose a new machine learning-based ensemble model with feature selection methods to detect phishing attacks. Also, an ablation study is presented to measure the effect of different feature selection methods. The proposed model which we named as NaiveStackingSymmetric (NSS) is analyzed using the widely used accuracy (ACC), the area under curve (AUC), and F-score metrics as well as the polygon area metric (PAM), and it is shown that it outperforms other studies in the literature using the same dataset.

Keywords

• Phishing attack
• ensemble learning
• malicious URL
• stacking
• information security

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