Principled Uncertainty Decomposition With Bayesian Ensemble Transformers for Trustworthy Intrusion Detection

This research presents a new AI framework for network intrusion detection systems (IDS, which are tools that identify unauthorized access attempts on computer networks) that provides both accurate threat detection and reliable confidence levels in its predictions. The framework combines transformer models (a type of neural network architecture) with ensemble methods (combining multiple AI models for better results) to break down prediction uncertainty into two types: epistemic uncertainty (uncertainty from the model itself) and aleatoric uncertainty (uncertainty from noisy or incomplete data). Testing on four benchmark datasets shows the system achieves strong detection rates (77.55% to 97.00% F1-scores, a measure of accuracy) while maintaining good calibration (accurate confidence estimates) and remaining resilient to adversarial attacks (attempts to fool the AI with specially crafted malicious inputs).