Web Sitelerinde Gerçekleştirilen Oltalama Saldırılarının Yapay Zekâ Yaklaşımı ile Tespiti

Year 2021, Volume: 10 Issue: 4, 1603 - 1614, 31.12.2021

Mesut Toğaçar

https://doi.org/10.17798/bitlisfen.988001

Cited By: 1

Abstract

Oltalama, kişisel bilgilerin internet üzerinden çalınmasına yönelik gerçekleştirilen yazılım tabanlı saldırılardır. Oltalama saldırılarında genellikle kişilerin kimlik bilgileri, kullanıcı parolaları, kredi veya banka kartı bilgileri gibi özel bilgilerin ele geçirilmesi amaçlanır. Bunun için en uygun ortam olarak genelde özel yazılım kodları içeren web sitesi uygulamaları veya elektronik posta sistemleri tercih edilir. Bu tür net uygulamalarında gelen cezbedici görsel veya metin tabanlı iletiler bireyleri yemleyerek saldırıların gerçekleştirilmesini sağlar. Milyarlarca insanın etkileşim içerisinde olduğu internet ortamında bu tür saldırıların önlemini zamanında alabilmek için teknolojik gelişmelerle paralel hareket etmek gerekir. Son zamanlarda, yapay zekâ teknolojileri internet güvenliği alanında adını duyurmayı başarmıştır. Bu çalışmada, makine öğrenme yöntemleri ile 11 binin üzerinde web sitesi incelenmiş ve oltalama saldırısı yapan web siteleri tespit edildi. Veri seti, 30 web parametresinden oluşmaktadır ve açık erişimlidir. Makine öğrenmesi yöntemleri ile her bir web sitesi için 30 özellik incelendi; oltalama saldırısını gerçekleştiren web siteleri ile gerçekleştirmeyen web siteleri sınıflandırıldı. Sonuç olarak, en iyi test doğruluk başarısı Rastgele Orman yöntemi ile %96,53 oranında gerçekleştirildi.

Keywords

Kimlik Avı Dolandırıcılığı, Yapay Zekâ, Makine Öğrenmesi, Oltalama Saldırıları.

Detection of Phishing Attacks on Websites Using Artificial Intelligence Approach

Abstract

Phishing is software-based attacks on the stealing of personal information over the internet. In phishing attacks, it is generally aimed to capture private information such as personal identification information, user passwords, credit or debit card information. Website applications or electronic mail systems containing special software codes are generally preferred as the most suitable medium for this. In this kind of net applications, attractive visual or text based messages feed individuals and enable attacks. It is necessary to act in parallel with the technological developments in order to prevent such attacks on time in the internet environment where billions of people interact. Recently, artificial intelligence technologies have managed to make a name in the field of internet security. In this study, over 11 thousand websites were analyzed with machine learning methods and websites that made phishing attacks were determined. The dataset consists of 30 web parameters and is open access. With machine learning methods, 30 features were examined for each website; web sites that carry out the phishing attack and those that did not. As a result, the best test accuracy achievement was realized by Random Forest method at 96.53%.

Keywords

Phishing Scams, Artificial Intelligence, Machine Learning, Phishing Attacks.

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