Streaming Anomaly Detection for Cyber-Physical Systems

Cyber-physical and industrial systems produce continuous sensor data. Abnormal behaviour needs to be detected and presented quickly enough for operators to understand what is happening and decide whether intervention is necessary.

The best-performing setup achieved 98% classification accuracy in the academic project setting. More importantly, the work connected model output to an operational monitoring interface rather than leaving results inside a notebook.

• Sensor trends
• Classification status
• Anomaly indicators
• Time-based monitoring
• Operational visibility

• Simulated rather than live industrial data
• Academic test environment
• No long-term drift analysis
• No production alerting or fail-safe design
• Accuracy alone does not capture false-alarm cost
• Industrial deployment would require stronger security, latency and reliability testing

• Add precision, recall and cost-sensitive alert evaluation
• Evaluate concept drift
• Test streaming latency
• Add operator feedback
• Use real equipment data
• Implement alert thresholds and audit logs