WEKA Ortamını Kullanarak Derin Öğrenme ile Kimlik Hırsızı Web Sitelerinin Tahmini

Year 2021, Issue: 24, 35 - 41, 15.04.2021

Özlem Batur Dinler Canan Batur Şahin

https://doi.org/10.31590/ejosat.901465

Cited By: 4

Abstract

30 Aralık 2019’da, Çin’in Wuhan şehrinde görülen COVID-19 (Coranavirus) hastalığı, dünya çapında yayılarak küresel bir salgına yol açmıştır. Bu salgın, çok hızlı ve çok kolay bulaşabildiği için hükümetlerin almak zorunda kaldığı birtakım önlemler ve gönüllü karantina uygulamaları, kısa bir süre içerisinde dünya topluluklarının alışkanlıklarını önemli ölçüde değiştirmiştir. Bu değişim özellikle, uzaktan çalışma, uzaktan eğitim ve uzaktan alışveriş (e-ticaret) gibi uzaktaki etkinlikleri artırdı. Bu nedenle insanlar günlük ihtiyaçlarını karşılamak adına kullandıkları fiziksel platformları, hızlıca dijital platformlara taşıma gereksinimi duydular. Bu durumda beraberinde, dijital platformların hedef alındığı web kimlik hırsızlığı çevrimiçi siber saldırı türlerinde ciddi bir artış meydana getirmiştir. Kimlik avındaki artış ve kimlik hırzısı web sitelerinin artan hacmi, dünyadaki bilgilerin ve kuruluşların çeşitli siber saldırılara daha fazla maruz kalmasıyla sonuçlandı. Bu nedenle, 2019'daki COVID-19 salgınından sonra kimlik hırsızı web sitelerinin analizini tespit etmek, her zamankinden daha önemli hale geldi. Bu çalışmada web kimlik hırsızlığı analiz edilmekte ve Bilgi Analizi için Waikato Ortamı (Waikato Environment for Knowledge Analysis - WEKA) grafik kullanıcı arayüzünden (GUI) yararlanarak RF, SVM, MLP, k-NN ve DL’den oluşan beş popüler yöntem arasındaki sınıflandırma performansları karşılaştırılmaktadır. Eğitim ve test olarak ikiye ayrılan veri seti ile yapılan deneylerde RF ve DL yöntemleri diğer yöntemlere göre daha başarılı iken, k-NN, çapraz doğrulama kullanıldığında daha iyi performans elde etmiştir. Bunun olası nedeni, derin öğrnemeye yönelik basit bir yaklaşımdır. Bu çalışmanın, kimlik hırzısı web sitelerinin sınıflandırması için WEKA derin öğrenmeyi araştırmada rehberlik sağlayacağını umuyoruz.

Keywords

Makine öğrenimi,, Derin öğrenme,, WEKA,, DL4J derin öğrenme mimarisi,, Web Kimlik Hırsızlığı,, Covid-19.

Supporting Institution

ARACONF 2021

Prediction of Phishing Web Sites with Deep Learning Using WEKA Environment

Abstract

COVID-19 (Coronavirus) disease, observed in the city of Wuhan, China, on December 30, 2019, spread worldwide and caused a global epidemic. Since this epidemic can be transmitted very quickly and easily, some precautions and voluntary quarantine practices that governments have to take have significantly changed the habits of world communities in a short time. This change has especially increased distance activities, such as distance working, distance education, and distance shopping (e-commerce). Therefore, people have felt the need to quickly move the physical platforms they use to digital platforms to meet their daily needs. In this case, web phishing targeting digital platforms has led to a significant increase in online cyber attack types. The increase in phishing and the increasing volume of phishing websites have resulted in greater exposure of the world's information and organizations to various cyberattacks. Thus, after the COVID-19 pandemic in 2019, it has become more important than ever to detect phishing website analysis. In this study, performs the web phishing analysis and makes a comparison of classification performances among five popular methods: Random Forest (RF), Support Vector Machine (SVM), Multilayer Perception (MLP), k-Nearest Neighbour (k-NN), and Deep Learning (DL) by utilizing a Waikato Environment for Knowledge Analysis (WEKA) graphical user interface (GUI). In the experiments conducted with the data set divided into two as training and test, the RF and DL methods were more successful than the other methods compared, but k-NN, achieved a better performance when cross-validation was used. The possible reason for this is a simple approach toward deep learning. We hope the current study can provide guidance in investigating WEKA deep learning for web phishing classification.

Keywords

Machine learning,, Deep learning,, WEKA,, DL4J deep learning architecture,, Web Phishing,, COVID-19.

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