Advanced Cyber Deception Framework (ACDF): A Comprehensive Study
Mark Maldonado, Caleb Johnson, Manbir Gulati, Todd Jaspers, Paul F. Roysdon · 2024
Deception frameworks provide an effective environment for data collection on cyber criminals. Using deception techniques these frameworks help security professionals identify and deceive attackers. Information security is an increasingly complex problem as cyber attacks evolve and attackers become more competent in exploitation. Honeypots or honey networks provide an opportunity for counter-intelligence collection. The current State-of-the-Art (SotA) honeypot deployments are easily identified and cataloged by adversaries. Using machine learning, specifically a Tabular Masked Transformer (TabMT) model, we generate honeypots with a realistic host and network traffic to prolong engagement and improve intelligence gathering efforts.