Towards Real-Time Malware Classification Through Honeypot Analysis
Miguel Faísco, Ibéria Medeiros, Hans P. Reiser · 2025
The rising sophistication of cyberattacks demands innovative solutions to safeguard system security and dependability. Traditional honeypots, while essential for malware collection, fall short in addressing modern threats like file-less malware and multi-stage attacks. This work presents a novel high-interaction honeypot architecture with integrated real-time analysis that allows for more accurate machine learning-based malware classification. The preliminary results of the proposed honeypot showed that such a novel architecture can detect and classify malware.