Cyber-Physical System Security Surveillance using Knowledge Graph based Digital Twins - A Smart Farming Usecase
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2021-08-31
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S. S. L. Chukkapalli, N. Pillai, S. Mittal and A. Joshi, "Cyber-Physical System Security Surveillance using Knowledge Graph based Digital Twins - A Smart Farming Usecase," 2021 IEEE International Conference on Intelligence and Security Informatics (ISI), 2021, pp. 1-6, doi: 10.1109/ISI53945.2021.9624688.
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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Abstract
Rapid advancements in Cyber-Physical System (CPS) capabilities have motivated farmers to deploy this ecosystem on their farms. However, there is a growing concern among users regarding the security risks associated with CPS. Especially with rising number of cyber-attacks on CPS, such as modifying sensor readings, interrupting operations, etc. Therefore, this paper describes a security surveillance framework to detect deviations in the ecosystem by incorporating a digital twin supported anomaly detection model. The reason for incorporating digital twins is that they add value by enabling real-time monitoring of connected smart farms. We pre-process the collected data from sensors deployed on the smart farm setup. The pre-processed data is fused with our smart farm ontology to populate a knowledge graph. The generated graph is further queried to extract the necessary sensor data. We utilize the extracted normal data to train the anomaly detection model. Further, we tested our model if it identifies abnormal values from sensors by simulating anomalous use case scenarios specific to our ecosystem.