The goal of this project is to develop virtualized lab modules using docker containers on top of the existing Cyber lab exercises from the Naval Postgraduate School (Labtainers) that will make use of various machine learning algorithms for attack vectors and vulnerabilities, such as intrusion detection, malware analysis, spam filters, anomaly detection, etc. Supervised, unsupervised, and semi-supervised algorithms as well as clustering, classification, regression, time-series analysis, and deep learning methodologies will be employed from cybersecurity papers published in journal and conferences with real and synthetic data sets. Each lab module will provide step-by-step walk-through for the student as well as end-of-exercise programming exercises. The use of current problem formulations from ongoing real research will likely result in student-led submissions to peer-reviewed publications, as both PIs have significant experience in getting students involved in published research. The labs will be shared with other institutions per the target funding agency requirements.