Real-Time Anomaly Detection for Industrial Robotic Arms Using Edge Computing
IEEE Internet of Things Journal (IOTJ), Volume 12, Issue 15, pp 29696-29712. August 2025
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 communication protocols such as Bluetooth and ZigBee, creating an air-gapped layer of protection independent from primary control channels. 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 for protecting manufacturing environments.
Sensors positioned around industrial robotic arms observe temperature, vibration, light, and sound to detect anomalies. Edge analytics and state-of-the-art deep learning correlate these physical signatures with expected behaviour, enabling operators to receive early warnings before faults escalate, even when attackers spoof primary telemetry. This physical-layer monitoring complements traditional network defences by providing an independent source of ground truth.
The work has produced the CASPER platform, validated through experimental deployments. The project has contributed open datasets and toolkits for the broader research community, supporting reproducible experimentation in cyber-physical anomaly detection for industrial environments.
IEEE Internet of Things Journal (IOTJ), Volume 12, Issue 15, pp 29696-29712. August 2025
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