The WAM Systems Lab has a new conference paper accepted to appear:

D. Agnew, A. del Aguila, and J. McNair, “Enhanced Network Metric Prediction for Machine Learning-based Cyber Security of a Software-Defined UAV Relay Network,” IEEE Access Journal, 12(2024):54202-54219.

Zero-day attacks are newly developed cyberattacks that can cause significant harm to the functionality of a Software defined network based Unmanned Air Vehicle (SD-UAV) network. Due to the novelty of these attacks, there is a lack of data samples for machine learning model training and testing. To alleviate this problem, this paper provides a predictive queuing analysis of a SD-UAV network to predict network performance statistics of SD-UAV networks to enhance and train machine learning models.