Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Mingtao Wu, Zhengyi Song and Young B. Moon ()
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Mingtao Wu: Syracuse University
Zhengyi Song: Syracuse University
Young B. Moon: Syracuse University

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 3, No 10, 1123 pages

Abstract: Abstract CyberManufacturing system (CMS) is a vision for future manufacturing systems. The concept delineates a vision of advanced manufacturing system integrated with technologies such as Internet of Things, Cloud Computing, Sensors Network and Machine Learning. As a result, cyber-attacks such as Stuxnet attack will increase along with growing simultaneous connectivity. Now, cyber-physical attacks are new and unique risks to CMSs and modern cyber security countermeasure is not enough. To learn this new vulnerability, the cyber-physical attacks is defined via a taxonomy under the vision of CMS. Machine learning on physical data is studied for detecting cyber-physical attacks. Two examples were developed with simulation and experiments: 3D printing malicious attack and CNC milling machine malicious attack. By implementing machine learning methods in physical data, the anomaly detection algorithm reached 96.1% accuracy in detecting cyber-physical attacks in 3D printing process; random forest algorithm reached on average 91.1% accuracy in detecting cyber-physical attacks in CNC milling process.

Keywords: CyberManufacturing systems; Security; Additive manufacturing; Machine learning (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s10845-017-1315-5

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