Towards Real-Time Malware Classification Through Honeypot Analysis

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.