Deepfake Social Engineering Attacks: Detection and Prevention Framework

https://doi.org/10.5281/zenodo.19344195

Abstract

Deepfake technologies powered by artificial intelligence have significantly increased the sophistication and effectiveness of modern social engineering attacks. Using advanced generative models such as Generative Adversarial Networks (GANs) and deep neural networks, cybercriminals can create highly realistic synthetic audio, video, and images to impersonate executives, employees, or other trusted individuals within organizations. These manipulated media contents are increasingly being used in fraudulent schemes such as executive impersonation, voice phishing, financial manipulation, and disinformation campaigns. As a result, traditional cybersecurity systems that primarily focus on network-level threats often fail to detect such attacks because they exploit human trust rather than technical vulnerabilities. This research proposes an intelligent deepfake detection framework specifically designed to protect organizations in the United States from AI-driven social engineering threats. The proposed framework integrates multiple detection components, including deep learning–based media analysis, behavioral verification mechanisms, and multimedia forensic techniques to identify inconsistencies in facial movements, voice patterns, and contextual communication behaviors. A hybrid deep learning architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is used to analyze spatial and temporal features within multimedia data. Experimental evaluation demonstrates that the proposed hybrid detection model significantly improves detection accuracy and robustness compared to conventional machine learning approaches. The findings highlight the critical importance of combining AI-based detection technologies with organizational cybersecurity policies, employee awareness programs, and identity verification mechanisms. The proposed framework provides a proactive defense strategy to mitigate the growing risks associated with deepfake-enabled social engineering attacks in modern enterprise environments.