BXLL: A Bi-LSTM stacking ensemble with SDN-Driven honeypot engagement for adaptive IoT intrusion detection

This research paper presents BXLL, a system that combines Bi-LSTM (a type of neural network that processes data in both directions to recognize patterns) stacking with SDN-driven honeypots (fake systems designed to attract and study attackers) to detect unauthorized access attempts in IoT (internet of things, networks of connected devices) networks. The approach uses an ensemble method (combining multiple AI models to make better predictions) to improve the accuracy of identifying when someone is trying to break into IoT systems.