Abstract

Cybercrime has evolved into a pervasive and multifaceted threat, impacting both digital and physical realms. The proliferation of online communication platforms, notably the Darknet and Telegram, has been increasingly exploited by cybercriminals and terrorists to coordinate attacks, disseminate tools, and share knowledge. While networks like Tor and Telegram offer anonymity for legitimate users, they are frequently misused for illicit transactions involving drugs, weapons, and stolen data, intensifying the tension between privacy rights and cybersecurity imperatives (Kavallieros et al., 2021). By combining LLM-driven text analysis, visual recognition, CYEYE supports scalable, multi-case investigations and contributes new insights into cyber investigators' workflows and decision-making processes. This research advances the field of cyber intelligence by demonstrating how automated platforms can bridge the gap between data overload and actionable insight in the cybersecurity arena.

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