Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation
Despite considerable advancements in integrating the Internet of Things (IoT) and artificial intelligence (AI) within the industrial maintenance framework, the increasing reliance on these innovative technologies introduces significant vulnerabilities due to cybersecurity risks, potentially compromi...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2078-2489/15/11/740 |
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| author | Flora Amato Egidia Cirillo Mattia Fonisto Alberto Moccardi |
| author_facet | Flora Amato Egidia Cirillo Mattia Fonisto Alberto Moccardi |
| author_sort | Flora Amato |
| collection | DOAJ |
| description | Despite considerable advancements in integrating the Internet of Things (IoT) and artificial intelligence (AI) within the industrial maintenance framework, the increasing reliance on these innovative technologies introduces significant vulnerabilities due to cybersecurity risks, potentially compromising the integrity of decision-making processes. Accordingly, this study aims to offer comprehensive insights into the cybersecurity challenges associated with predictive maintenance, proposing a novel methodology that leverages generative AI for data augmentation, enhancing threat detection capabilities. Experimental evaluations conducted using the NASA Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset affirm the viability of this approach leveraging the state-of-the-art TimeGAN model for temporal-aware data generation and building a recurrent classifier for attack discrimination in a balanced dataset. The classifier’s results demonstrate the satisfactory and robust performance achieved in terms of accuracy (between 80% and 90%) and how the strategic generation of data can effectively bolster the resilience of intelligent maintenance systems against cyber threats. |
| format | Article |
| id | doaj-art-6142a5a258764da7b1ac9938cc5bdc0b |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-6142a5a258764da7b1ac9938cc5bdc0b2025-08-20T02:05:02ZengMDPI AGInformation2078-24892024-11-01151174010.3390/info15110740Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data AugmentationFlora Amato0Egidia Cirillo1Mattia Fonisto2Alberto Moccardi3Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDespite considerable advancements in integrating the Internet of Things (IoT) and artificial intelligence (AI) within the industrial maintenance framework, the increasing reliance on these innovative technologies introduces significant vulnerabilities due to cybersecurity risks, potentially compromising the integrity of decision-making processes. Accordingly, this study aims to offer comprehensive insights into the cybersecurity challenges associated with predictive maintenance, proposing a novel methodology that leverages generative AI for data augmentation, enhancing threat detection capabilities. Experimental evaluations conducted using the NASA Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset affirm the viability of this approach leveraging the state-of-the-art TimeGAN model for temporal-aware data generation and building a recurrent classifier for attack discrimination in a balanced dataset. The classifier’s results demonstrate the satisfactory and robust performance achieved in terms of accuracy (between 80% and 90%) and how the strategic generation of data can effectively bolster the resilience of intelligent maintenance systems against cyber threats.https://www.mdpi.com/2078-2489/15/11/740artificial intelligencepredictive maintenancesecure artificial intelligence |
| spellingShingle | Flora Amato Egidia Cirillo Mattia Fonisto Alberto Moccardi Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation Information artificial intelligence predictive maintenance secure artificial intelligence |
| title | Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation |
| title_full | Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation |
| title_fullStr | Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation |
| title_full_unstemmed | Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation |
| title_short | Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation |
| title_sort | detecting adversarial attacks in iot enabled predictive maintenance with time series data augmentation |
| topic | artificial intelligence predictive maintenance secure artificial intelligence |
| url | https://www.mdpi.com/2078-2489/15/11/740 |
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