Honeypot Feedback Loop Cuts ML Firewall False Positives by 99%, Study Finds

Researchers published an IEEE paper proposing a dual-machine intrusion prevention architecture that addresses the high false positive rates plaguing conventional ML-based network security systems. The system pairs an inline monitoring component running a hybrid ensemble of Random Forest, XGBoost, and Isolation Forest models with a dedicated honeypot sandbox that observes borderline traffic behaviorally instead of dropping it outright. A tri-state scoring mechanism routes suspicious flows to the honeypot for validation before any blocking rule is enforced, with a 24-hour canary-testing window used to verify new rules at scale. Tested against roughly 939,000 network flows drawn from three benchmark datasets spanning 1999 to 2017, the architecture achieved 97.79% accuracy and processed around 71,000 flows per second on standard CPU hardware. The pipeline reduced the intermediate false positive rate from 1.13% down to a projected 0.0068%, potentially making automated inline blocking practical for high-throughput enterprise networks.