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Maranachakanahalli Dhananjaya, Jagadish (2024) Real-Time Detection of Social Engineering Threats in Social Media Posts. Masters thesis, Dublin, National College of Ireland.
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Abstract
Social engineering attacks utilize online information, such as data from social media platforms, to indirectly obtain personal information. This method poses significant security risks by exploiting publicly available data to piece together sensitive information, making it harder for individuals to recognize and prevent such attacks. This research shows how we can predict susceptibility of social engineering by analysing social media posts. This research utilizes the YOLO (You Only Look Once) model, a real-time object detection system that processes an entire image in a single pass by predicting bounding boxes and class probabilities simultaneously, to detect objects such as laptops and dogs. Additionally, natural language processing is employed to analyse text for information like dates of birth and names, which are utilized in social engineering attacks. The application combines this analysis to provide users with immediate warnings by alerting them to potential security threats before they post on social media. This integrated approach, using both visual and text data, enhances the ability to predict social engineering attacks. This research contributes to cybersecurity by offering a proactive tool for protecting personal and corporate information shared on social media.
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