Work

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

Year: 2024

Type: article

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.

Source: Information

DOI: 10.3390/info15110740

Authors: Flora Amato, Egidia Cirillo, Mattia Fonisto, Alberto Moccardi

Institution: University of Naples Federico II

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,819