SİBER SAVUNMADA AKILLI YÖNTEMLER: WEB SAYFALARINDA MAKİNE ÖĞRENİMİ TABANLI KİMLİK AVI TESPİTİ

Year 2024, , 416 - 429, 30.06.2024

Remzi Gürfidan

https://doi.org/10.21923/jesd.1458955

Abstract

Web sayfalarında oltalama saldırısı, internet kullanıcılarının kişisel ve hassas bilgilerini çalmayı amaçlayan kötü niyetli bir saldırı türüdür. Oltalama saldırıları genellikle e-posta, SMS, sosyal medya mesajları veya web siteleri gibi çeşitli iletişim kanalları aracılığıyla gerçekleştirilir. Kullanıcılar devlet kurumları, bankalar, çevrimiçi alışveriş siteleri gibi güvenilir kuruluşların sahte web sayfalarına yönlendirilir ve kişisel bilgilerini girmeleri istenir. Bu sahte web sayfaları orijinal sitelere oldukça benzeyebilir ve kullanıcıları yanıltmak için tasarlanmıştır. Bu çalışmada, web sayfalarının kimlik avı tehdidini tespit etmek için makine öğrenimi yöntemlerini kullandık ve bu alanda önemli bir ilerleme kaydettik. Altı farklı makine öğrenimi algoritmasının kapsamlı analizi, Extra Trees algoritmasının en başarılı sonuçları verdiğini gösterdi. Bu başarıyı daha da artırmak için Extra Trees algoritmasında ince ayarlar yaptık ve doğru sınıflandırma başarısını %97,9'a çıkardık. Gelecekteki çalışmalarda, bu teknolojinin kötü amaçlı yazılım tespiti veya kimlik avı saldırılarının önlenmesi gibi alanlarda kullanımını araştırmak için veri kümesini diğer makine öğrenimi yöntemlerini içerecek şekilde genişletmek istiyoruz. Bu, siber güvenlik alanında daha kapsamlı koruma sağlamaya yönelik çok önemli bir adım olacaktır.

Keywords

Phishing Saldırısı, Siber Güvenlik, Makine Öğrenmesi, Web Site Phishing Saldırı Tespiti, Extra Trees

INTELLIGENT METHODS IN CYBER DEFENCE: MACHINE LEARNING BASED PHISHING ATTACK DETECTION ON WEB PAGES

Abstract

Phishing attack on web pages is a type of malicious attack that aims to steal personal and sensitive information of internet users. Phishing attacks are usually conducted through various communication channels such as email, SMS, social media messages or websites. Users are directed to fake web pages of trusted organizations such as government agencies, banks, online shopping sites, etc. and asked to enter their personal information. These fake web pages may look remarkably like the original sites and are designed to mislead users. In this study, we used machine learning methods to detect the phishing attack threat of web pages and made significant progress in this area. Extensive analysis of six different machine learning algorithms showed that the Extra Trees algorithm yielded the most successful results. To further improve this success, we fine-tuned the Extra Trees algorithm and increased the correct classification success to 97.9%. In future studies, we would like to expand the dataset to include other machine learning methods to investigate the use of this technology in areas such as malware detection or the prevention of phishing attacks. This would be a crucial step towards providing more comprehensive protection in the field of cybersecurity.

Keywords

Phishing Attack, Cyber Security, Machine Learning, Web Site Phishing Attack Detection, Extra Trees

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