View in EDS

Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation.

Saved in:
Bibliographic Details
Title: Detecting Adversarial Attacks in IoT-Enabled Predictive Maintenance with Time-Series Data Augmentation.
Authors: Amato, Flora, Cirillo, Egidia, Fonisto, Mattia, Moccardi, Alberto
Source: Information; Nov2024, Vol. 15 Issue 11, p740, 18p
Subject Terms: GENERATIVE artificial intelligence, ARTIFICIAL intelligence, TECHNOLOGICAL innovations, DATA augmentation, CYBERTERRORISM
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:20782489
DOI:10.3390/info15110740