Context Aware Security for Industrial Control Systems

Builds CASPER, a context-aware anomaly detection platform that observes industrial robots with independent IoT sensors to spot attacks that evade traditional network monitoring.

Industrial Cyber-Physical Systems (ICPSs) manage critical manufacturing processes where digital controls and physical behaviour are tightly coupled. Sophisticated adversaries exploit this connection by manipulating sensor readings while controlling connected devices, evading traditional network traffic analysis. CASPER (Context Aware Security for cyber Physical Edge Resources) addresses this vulnerability by deploying a secondary, low-cost IoT sensor network that operates on separate protocols such as Bluetooth and ZigBee, creating an air-gapped layer of protection.

The project pursues four main objectives: reviewing current ICPSs from cybersecurity perspectives, developing a reconfigurable IoT sensing infrastructure for analytics deployment, augmenting cyberattack detection through physical and behavioural monitoring, and evaluating the effectiveness of a context-aware, dynamically adaptive IoT edge network. Sensors around robotic arms observe temperature, vibration, light, and sound, while edge analytics and state-of-the-art deep learning correlate these physical signatures with expected behaviour. This approach enables operators to receive early warnings before faults escalate, even when attackers spoof primary telemetry.