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A resilient cyber-physical demand forecasting system for critical infrastructures against stealthy false data injection attacks
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Gheyas, Iffat, Epiphaniou, Gregory, Maple, Carsten and Lakshminarayana, Subhash (2022) A resilient cyber-physical demand forecasting system for critical infrastructures against stealthy false data injection attacks. Applied Sciences, 12 (19). 10093. doi:10.3390/app121910093 ISSN 2076-3417.
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Official URL: https://doi.org/10.3390/app121910093
Abstract
The safe and efficient function of critical national infrastructure (CNI) relies on the accurate demand forecast. Cyber-physical system-based demand forecasting systems (CDFS), typically found in CNI (such as energy, water, and transport), are highly vulnerable to being compromised under false data injection attacks (FDIAs). The problem is that the majority of existing CDFS employ anomaly-based intrusion detection systems (AIDS) to combat FDIAs. Since the distribution of demand time series keeps changing naturally with time, AIDS treat a major change in the distribution as an attack, but this approach is not effective against colluding FDIAs. To overcome this problem, we propose a novel resilient CDFS called PRDFS (Proposed Resilient Demand Forecasting System). The primary novelty of PRDFS is that it uses signature-based intrusion detection systems (SIDS) that automatically generate attack signatures through the game-theoretic approach for the early detection of malicious nodes. We simulate the performance of PRDFS under colluding FDIA on High Performance Computing (HPC). The simulation results show that the demand forecasting resilience of PRDFS never goes below 80%, regardless of the percentage of malicious nodes. In contrast, the resilience of the benchmark system decreases sharply from about 99% to less than 30%, over the simulation period as the percentage of malicious nodes increases.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Cooperating objects (Computer systems), Computer networks -- Security measures, Intrusion detection systems (Computer security), Computer security | ||||||
Journal or Publication Title: | Applied Sciences | ||||||
Publisher: | MDPI | ||||||
ISSN: | 2076-3417 | ||||||
Official Date: | 7 October 2022 | ||||||
Dates: |
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Volume: | 12 | ||||||
Number: | 19 | ||||||
Number of Pages: | 30 | ||||||
Article Number: | 10093 | ||||||
DOI: | 10.3390/app121910093 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 9 November 2022 | ||||||
Date of first compliant Open Access: | 10 November 2022 | ||||||
RIOXX Funder/Project Grant: |
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