Modeling of Social Engineering Attacks GAN Based Email Generation and Detection

Abstract

This study presents a simulation approach based on Generative Adversarial Network (GAN) to increase the realism of phishing emails and test their defense mechanisms. Human-like phishing emails were generated using an LSTM-based generator and a BERT-based discriminator, and the success of distinguishing these contents by detection tools and human observers was analyzed. The model, trained on a dataset of 1000 balanced emails, was able to distinguish real and fake content with over 95% accuracy. The results show that GAN architecture can be a powerful tool in understanding social engineering attacks and testing cyber defense systems.

Keywords

• Phishing
• Generative Adversarial Networks (GAN)
• LSTM
• Social Engineering

Sosyal Mühendislik Saldırılarının Modellemesi GAN Tabanlı E-Posta Üretimi ve Tespiti

Abstract

Bu çalışma, phishing (oltalama) e-postalarının gerçekçiliğini artırmak ve savunma mekanizmalarını test etmek amacıyla, Generative Adversarial Network (GAN) tabanlı bir simülasyon yaklaşımı sunmaktadır. LSTM tabanlı bir generator ve BERT tabanlı bir discriminator kullanılarak, insan benzeri phishing e-postaları üretilmiş ve bu içeriklerin tespit araçları ve insan gözlemciler tarafından ayırt edilme başarısı analiz edilmiştir. 1000 dengeli e-posta içeren veri seti ile eğitilen model, %95'in üzerinde doğruluk oranıyla gerçek ve sahte içerikleri ayırt edebilmiştir. Sonuçlar, GAN mimarisinin sosyal mühendislik saldırılarını anlamada ve siber savunma sistemlerini test etmede güçlü bir araç olabileceğini göstermektedir.

Keywords

• Phishing
• Generative Adversarial Networks (GAN)
• LSTM
• Sosyal Mühendislik

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