Kimlik Hırsızı Web Sitelerinin Farklı DeepLearning4J Modelleri ile Performanslarının Karşılaştırılması

Year 2021, Issue: 28, 425 - 431, 30.11.2021

Özlem Batur Dinler , Canan Batur Şahin , Laith Abualigah

https://doi.org/10.31590/ejosat.1004778

Cited By: 1

Abstract

İlk olarak 2019’da Çin’in Wuhan şehrinde görülen yeni tip koranavirüs (COVID-19 ) hastalığı nedeniyle 11 Mart 2020 tarihinde Dünya Sağlık Örgütü (DSÖ) tarafından pandemi ilan edilmiştir. Dünya genelinde hâlâ etkisi devam etmekte olan bu salgın, kısa sürede tüm dünya toplumunun gündelik yaşam aktivitelerini ve alışkanlıklarını hızlı bir şekilde değiştirerek digital ortam uygulamalarına doğru kaydırmıştır. Bu doğrultuda, artan siber saldırı atakları ve yaşanan veri ihlalleri salgın toplumu için büyük bir risk oluşturmuştur. Bu bağlamda, dijital ortam uygulamalarının güvenliği COVID-19 salgını ile çok daha önemli bir sorun haline gelmiştir. Bu sorun özellikle kimlik hırsızı web siteleri üzerinde gözlenmiştir. Web kimlik hırsızlığı, güvenilir kurumları taklit ederek kişilerin ad, soyad, şifre ve kredi kartı numaraları gibi kişisel bilgileri çalma yöntemidir. Bu, bilginin ifşa olmasına ve mali zarara neden olacaktır. Çalışmanın odağı, kimlik hırsızı web sitelerinin tanımlanması amacı ile kullanılan birkaç DeepLearning4j (DL4j) modeline dayanmaktadır. Bununla birlikte, çalışmanın temel amacı, değerlendirme metriklerinin performanslarını iyileştirmek amacı ile DeepLearning4J (DL4J) modellerinin etkinliğini verimli bir şekilde izlemektir.

Keywords

COVID-19, DL4J, Siber Saldırı, Web, Kimlik Hırsızlığı.

Comparison of Performance of Phishing Web Sites with Different DeepLearning4J Models

Abstract

Due to the new type of coronavirus (COVID-19) disease, which was first seen in Wuhan, China in 2019, a pandemic was declared by the World Health Organization (WHO) on March 11, 2020. The pandemic, which is still in effect throughout the world, has changed the daily life activities and habits of the whole world community in a short time and shifted them towards digital media applications. Accordingly, increasing cyber-attack attacks and data breaches have created great risk for the pandemic society. In this context, the security of digital media applications has become a much more important issue with the COVID-19 outbreak. The issue has been observed especially on phishing websites. Web phishing is the practice of stealing personal information such as name, last name, password, and credit card numbers of individuals by imitating a reputable business. It will result in the exposure of information and the financial damage. The focus of the study is based on several DeepLearning4j (DL4j) models used to identify phishing websites. However, the main purpose of the study is to efficiently monitor the effectiveness of DeepLearning4J (DL4J) models with the aim of improving the performance of evaluation metrics.

Keywords

COVID-19, DL4J, Cyber attack, Web, Phishing

References

• Batur Dinler., Ö, Batur Şahin., C. (2021). Prediction of Phishing Web Sites with Deep Learning Using WEKA Environment. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Özel Sayı, 2021 (ARACONF), 35-42.
• Ullah., A, Batur Dinler., Ö, and Batur Şahin., C. (2021). The Effect of Technology and Service on Learning Systems During the COVID-19 Pandemic. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Özel Sayı 2021 (ICAENS), 28,106-114, 28.
• Graphus Kaseya Company, https://www.graphus.ai/blog/10-facts-about-phishing-in-2021-that-you-need-to-see.
• Yang., S.(2020). Research on web site phishing detection based on LSTM RNN. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2020), 284-288.DOI: 10.1109 / ITNEC48623.2020.9084799.
• Batur Şahin., C, Batur Dinler., Ö, Abualigah., L. (2021). Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features. Applied Intelligence, https://doi.org/10.1007/s10489-021-02324-3.
• Adebowale., M.A, Lwin., K.T, and Hossain., M.A. (2020). Intelligent phishing detection scheme using deep learning algorithms. Journal of Enterprise Information Management ©Emerald Publishing Limited .1741-0398. DOI:10.1108/JEIM-01-2020-0036.
• Khan., M.F, Rana, B.L. (2021). Detection of Phishing Websites Using Deep Learning Techniques. Turkish Journal of Computer and Mathematics Education. Vol.12 No.10, 3880- 3892.
• Barstugan, M., Ozkaya, U., & Ozturk, S. (2020). Coronavirus (COVID-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424.
• Benavides, E., Fuertes, W., Sanchez, S., & Sanchez, M. (2020). Classification of phishing attack solutions by employing deep learning techniques: A systematic literature review. In Developments and advances in defense and security (pp. 51–64). Springer.
• Maurya, S., & Jain, A. (2020). Deep learning to combat phishing. Journal of Statistics and Management Systems, pp. 1–13.
• Shie, E. W. S. (2020). Critical analysis of current research aimed at improving detection of phishing attacks. Selected computing research papers, p. 45.
• Abdelhamid et al., (2014). Phishing detection based associative classification data mining. Expert System With Applications(ESWA),41, 5948-5959.
• Batur Dinler, Ö.; Aydın N. An Optimal Feature Parameter Set Based on Gated Recurrent Unit Recurrent Neural Networks for Speech Segment Detection. Appl. Sci. 2020, 10, 1273. https://doi.org/10.3390/app10041273.
• Şahin, C., and Dírí B. (2019), Robust Feature Selection with LSTM Recurrent Neural Networks for Artificial Immune Recognition System, IEEE Access, Vol.7, pp. 24165 – 24178.
• Lang, S., Bravo-Marquez, F., Beckham, C., Hall, M., Frank, E. (2019), WekaDeeplearning4j: A Deep Learning Package for Weka based on DeepLearning4j, Knowl.- Based Syst.178, 48–50. [CrossRef]
• Frank, E., Hall, M.A., Witten, I.H. (2016), The Weka Workbench, 4th ed.; Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann: Burlington, MA, USA.
• Zainudin, Z., Shamsuddin, S. and Hasan, S. (2019). Deep Learning for image processing in WEKA environment. Int. J. Advance Soft Compu. Appl, Vol. 11, No. 1, March 2019, ISSN 2074-282.