TrustData: trustworthy and secured data collection for event detection in industrial cyber-physical system (In Press)
Industrial Cyber-physical System (ICPS) is utilized for monitoring critical events such as structural equipment conditions in industrial environments. Such a system can easily be a point of attraction for the cyberattackers, in addition to system faults, severe resource constraints (e.g., bandwidth...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/26311/ http://umpir.ump.edu.my/id/eprint/26311/ http://umpir.ump.edu.my/id/eprint/26311/ http://umpir.ump.edu.my/id/eprint/26311/1/TrustData%20-%20Trustworthy%20and%20Secured%20Data%20Collection%20for%20Event%20Detection%20in%20Industrial%20Cyber-Physical%20System.pdf |
| Summary: | Industrial Cyber-physical System (ICPS) is utilized for monitoring critical events such as structural equipment conditions in industrial environments. Such a system can easily be a point of attraction for the cyberattackers, in addition to system faults, severe resource constraints (e.g., bandwidth and energy), and environmental problems. This makes data collection in the ICPS untrustworthy, even the data are altered after the data forwarding. Without validating this before data aggregation, detection of an event through the aggregation in the ICPS can be difficult. This paper introduces TrustData, a scheme for highquality data collection for event detection in the ICPS referred to as “Trustworthy and Secured Data Collection” scheme. It alleviates authentic data for accumulation at groups of sensor devices in the ICPS. Based on the application requirements, a reduced quantity of data is delivered to an upstream node, say, a cluster head. We consider this data might have sensitive information, which is vulnerable to being altered before/after transmission. The contribution of this paper is threefold. First, we provide the concept of TrustData to verify whether or not the acquired data is trustworthy (unaltered) before transmission, and whether or not the transmitted data is secured (data privacy is reserved) before aggregation. Second, we utilize a general measurement model that helps to verify acquired signal untrustworthy before transmitting towards upstream nodes. Finally, we provide an extensive performance analysis through real-world data set and our results prove the effectiveness of the TrustData. |
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