By Dr. Yaa Acquaah
Cyber-Physical Systems (CPS) are critical to modern infrastructure — from energy grids to water systems. But they also present a unique challenge for anomaly detection because they blend complex physical processes with digital control and communication.
CPS are dynamic by design. Sensor readings vary with time, temperature, flow rates, and user demand — so “normal” isn’t static. This makes traditional anomaly detection techniques prone to false positives.
Collecting real cyberattack data from live CPS environments is rare due to safety and confidentiality concerns. As a result, supervised models lack labeled data, and even unsupervised approaches suffer from poorly representative training sets.
Distinguishing between a failing sensor and a real cyber-attack is tricky. Both produce strange data — but only one is malicious. Without robust feature engineering and signal processing, models may misclassify these events.
Models trained on one system don’t always work well on another. For example, anomaly patterns in a gas pipeline system may differ entirely from those in a water distribution network. Cross-domain learning and transferability are active research areas.
CPS anomalies can cause damage quickly. Detection models must not only be accurate but also fast. Real-time streaming, edge deployment, and model interpretability are vital concerns for production systems.
I focus on addressing these challenges by:
The future of CPS security lies in combining resilient data generation, smart models, and real-world validation. I’m excited to contribute to that future.