Hybrid Cyber-Physical Intrusion Detection System for Smart Manufacturing


  • Jeremy Potts Tennessee Technological University
  • Muhammad Ismail Department of Computer Science, Tennessee Tech University




Smart Manufacturing, Intrusion Detection Systems, Cyber-Physical Systems, Cyber-Physical Data Fusion


Smart manufacturing is an important part of our critical infrastructure and is the current age of industry where physical components such as robotic arms, 3-D Printers, CNC machine, etc. are all interconnected and remotely controlled or automated to provide a major boost in efficiency. While being more effective, the cyber-physical integration expands the attack surface of these systems for any potential threats to act on and exploit. This integration also creates gaps in the current intrusion detection systems (IDS) and the research of such systems as they focus on either the cyber or physical components of these system, which leaves blind spots when an attack can only be detected by using either cyber or physical features. This paper fill that research gap by creating a cyber-physical testbed, launching denial of service and physical hijacking attacks, collecting benign and malicious data, and creating a hybrid IDS using K-Nearest Neighbors and Decision Tree models that consider both cyber and physical features. Our proposed hybrid IDS achieves an accuracy of 97.2% which was roughly the same as separate cyber and physical IDSs, but there was a significant boost in precision (98.4%), recall (94.2%), and F1 score (96.1%) when using the hybrid IDS compared to the separate IDSs.




How to Cite

Potts, J., & Ismail, M. (2024). Hybrid Cyber-Physical Intrusion Detection System for Smart Manufacturing. The International FLAIRS Conference Proceedings, 37(1). https://doi.org/10.32473/flairs.37.1.135587



Special Track: Security, Privacy, Trust and Ethics in AI