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Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods
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DOI：10.1007/s10845-017-1315-5.png)
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摘要
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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.
Keyword:
CyberManufacturing systems
Security
Additive manufacturing
Machine learning
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