ScamChatBot: An End-to-End Analysis of Fake Account Recovery on Social Media via Chatbots
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910754144256000 |
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| author | Acharya, Bhupendra Sautter, Dominik Saad, Muhammad Holz, Thorsten |
| author_facet | Acharya, Bhupendra Sautter, Dominik Saad, Muhammad Holz, Thorsten |
| contents | Social media platforms have become the hubs for various user interactions covering a wide range of needs, including technical support and services related to brands, products, or user accounts. Unfortunately, there has been a recent surge in scammers impersonating official services and providing fake technical support to users through these platforms. In this study, we focus on scammers engaging in such fake technical support to target users who are having problems recovering their accounts. More specifically, we focus on users encountering access problems with social media profiles (e.g., on platforms such as Facebook, Instagram, Gmail, and X) and cryptocurrency wallets. The main contribution of our work is the development of an automated system that interacts with scammers via a chatbot that mimics different personas. By initiating decoy interactions (e.g., through deceptive tweets), we have enticed scammers to interact with our system so that we can analyze their modus operandi. Our results show that scammers employ many social media profiles asking users to contact them via a few communication channels. Using a large language model (LLM), our chatbot had conversations with 450 scammers and provided valuable insights into their tactics and, most importantly, their payment profiles. This automated approach highlights how scammers use a variety of strategies, including role-playing, to trick victims into disclosing personal or financial information. With this study, we lay the foundation for using automated chat-based interactions with scammers to detect and study fraudulent activities at scale in an automated way. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ScamChatBot: An End-to-End Analysis of Fake Account Recovery on Social Media via Chatbots Acharya, Bhupendra Sautter, Dominik Saad, Muhammad Holz, Thorsten Cryptography and Security Social media platforms have become the hubs for various user interactions covering a wide range of needs, including technical support and services related to brands, products, or user accounts. Unfortunately, there has been a recent surge in scammers impersonating official services and providing fake technical support to users through these platforms. In this study, we focus on scammers engaging in such fake technical support to target users who are having problems recovering their accounts. More specifically, we focus on users encountering access problems with social media profiles (e.g., on platforms such as Facebook, Instagram, Gmail, and X) and cryptocurrency wallets. The main contribution of our work is the development of an automated system that interacts with scammers via a chatbot that mimics different personas. By initiating decoy interactions (e.g., through deceptive tweets), we have enticed scammers to interact with our system so that we can analyze their modus operandi. Our results show that scammers employ many social media profiles asking users to contact them via a few communication channels. Using a large language model (LLM), our chatbot had conversations with 450 scammers and provided valuable insights into their tactics and, most importantly, their payment profiles. This automated approach highlights how scammers use a variety of strategies, including role-playing, to trick victims into disclosing personal or financial information. With this study, we lay the foundation for using automated chat-based interactions with scammers to detect and study fraudulent activities at scale in an automated way. |
| title | ScamChatBot: An End-to-End Analysis of Fake Account Recovery on Social Media via Chatbots |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2412.15072 |