Abstract
Digital transformation increasingly gains broad attentions from all the world and particularly studies on artificial intelligence, big data, cloud, and mobile are currently conducted. In addition, research based on ambient intelligence are also performed. Everything including condition information of all objects are shared on real time in AMI environment and all locations and objects are equipped with sensors. It acts intelligently such as decision-making. As sensors are equipped in locations and objects and connected with high-performance computer networks, users can receive information at any time and anywhere. In particular, the adoption of smart factory that turns all phases into automation and intellectualization based on cyber-physical system technology is proliferating. However, unexpected problems are likely to take place due to high complexity and uncertainty of smart factory. Thus, it is very likely to end manufacturing process, trigger malfunction, and leak important information. Although the necessity of analyzing threats to smart factory and systematic management is emphasized, there is insufficient research. In this paper, machine learning and context-aware intrusion detection system was built. The established system was effective to detection rate of anomaly signs and possibility of process achievement compared to the previous system.
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Acknowledgements
This work was supported by the ICT R&D program of MSIP/IITP. [IITP-2016-B0717-16-0119, Integrated Solution for secured management and Secure Gateway Solution for safe smart factory].
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Park, ST., Li, G. & Hong, JC. A study on smart factory-based ambient intelligence context-aware intrusion detection system using machine learning. J Ambient Intell Human Comput 11, 1405–1412 (2020). https://doi.org/10.1007/s12652-018-0998-6
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DOI: https://doi.org/10.1007/s12652-018-0998-6