Unsupervised ML Based Anomaly Detection for Securing BB84 QKD Against Intercept-Resend Attacks and Channel Noise: OC-SVM, iForest, and Ex-iForest

Abstract

Quantum Key Distribution (QKD) using the BB84 protocol offers the potential for information-theoretic security based upon the principles of quantum mechanics; however, eavesdropping mechanisms such as intercept-resend attacks and ambient noise sources that degrade quantum channel fidelity may render such systems exploitable. We present a oneclass machine learning approach to detect two categories of threats present within BB84 quantum channels: (i) interceptresend eavesdropping committed by an adversarial actor (Eve), and (ii) six types of channel noise. We have trained three unsupervised anomaly detectors (One-Class Support Vector Machine (OC-SVM), Isolation Forest (iForest), and Extended Isolation Forest (Ex-iForest)) on a dataset consisting of 1000 instances (940 clean and 60 anomalous) and tested each of them on separate test sets of 100 instances. With respect to the detection of adversarial attacks, Ex-iForest outperformed both OC-SVM and iForest; it achieved 95.00% accuracy, precision, recall, and F1-score, and when detecting channel noise, it achieved an overall accuracy, precision, recall, and F1-score of 92.00%. Feature importance analysis confirmed that the Quantum Bit Error Rate (QBER) and the fidelity of polarization measurement were the two most discriminative features for adversarial detection while fluctuations of photon detection counts and timing jitter were the two most informative features for channel noise discrimination. These results suggest that ExiForest is a viable, robust, and efficient solution for real-time anomaly detection systems in BB84-based quantum communication systems.