TrustData: Trustworthy and Secured Data Collection for Event Detection in Industrial Cyber-Physical System

• Published in: IEEE transactions on industrial informatics 2020-05, Vol.16 (5), p.3311-3321
• Main Authors: Tao, Hai, Bhuiyan, Md Zakirul Alam, Rahman, Md Arafatur, Wang, Tian, Wu, Jie, Salih, Sinan Q., Li, Yafeng, Hayajneh, Thaier
• Format: Article
• Language: English
• Subjects: Agglomeration; Data acquisition; Data collection; Data management; Data privacy; data trustworthiness; Event detection; fault tolerance; industrial cyber-physical environments; industrial event monitoring; Informatics; Monitoring; privacy; Reliability; Security; Trustworthiness; Upstream
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2950192

In this article, an 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 article introduces TrustData, a scheme for high-quality data collection for event detection in the ICPS, referred to as "Trust worthy 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 that these data might have sensitive information, which is vulnerable to being altered before/after transmission. The contribution of this article is threefold. First, we provide the concept of TrustData to verify whether or not the acquired data are trustworthy (unaltered) before transmission, and whether or not the transmitted data are secured (data privacy is preserved) before aggregation. Second, we utilize a general measurement model that helps to verify acquired signal untrustworthy before transmitting toward upstream nodes. Finally, we provide an extensive performance analysis through a real-world dataset, and our results prove the effectiveness of TrustData.