  The protection of Industrial Control Systems (ICS) that are employed in
public critical infrastructures is of utmost importance due to catastrophic
physical damages cyberattacks may cause. The research community requires
testbeds for validation and comparing various intrusion detection algorithms to
protect ICS. However, there exist high barriers to entry for research and
education in the ICS cybersecurity domain due to expensive hardware, software,
and inherent dangers of manipulating real-world systems. To close the gap,
built upon recently developed 3D high-fidelity simulators, we further showcase
our integrated framework to automatically launch cyberattacks, collect data,
train machine learning models, and evaluate for practical chemical and
manufacturing processes. On our testbed, we validate our proposed intrusion
detection model called Minimal Threshold and Window SVM (MinTWin SVM) that
utilizes unsupervised machine learning via a one-class SVM in combination with
a sliding window and classification threshold. Results show that MinTWin SVM
minimizes false positives and is responsive to physical process anomalies.
Furthermore, we incorporate our framework with ICS cybersecurity education by
using our dataset in an undergraduate machine learning course where students
gain hands-on experience in practicing machine learning theory with a practical
ICS dataset. All of our implementations have been open-sourced.
