Version 1
: Received: 22 August 2024 / Approved: 23 August 2024 / Online: 23 August 2024 (10:11:47 CEST)
How to cite:
Pacheco, J.; Benitez, V. H.; Pérez, G.; Brau, A. Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems. Preprints2024, 2024081710. https://doi.org/10.20944/preprints202408.1710.v1
Pacheco, J.; Benitez, V. H.; Pérez, G.; Brau, A. Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems. Preprints 2024, 2024081710. https://doi.org/10.20944/preprints202408.1710.v1
Pacheco, J.; Benitez, V. H.; Pérez, G.; Brau, A. Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems. Preprints2024, 2024081710. https://doi.org/10.20944/preprints202408.1710.v1
APA Style
Pacheco, J., Benitez, V. H., Pérez, G., & Brau, A. (2024). Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems. Preprints. https://doi.org/10.20944/preprints202408.1710.v1
Chicago/Turabian Style
Pacheco, J., Guillermo Pérez and Agustín Brau. 2024 "Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems" Preprints. https://doi.org/10.20944/preprints202408.1710.v1
Abstract
In today's technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers to scrutinize sensor data meticulously. Our methodology aims to detect and isolate malfunctions, misuse, or any anomalies before they permeate the system, potentially causing widespread disruption. By leveraging the power of wavelet-based analysis, we embed computational intelligence directly into the microcontrollers, enabling them to monitor and validate their outputs in real-time. This proactive anomaly detection framework is designed to distinguish between normal and aberrant sensor behaviors, thereby safeguarding the system from erroneous data propagation. Our approach significantly enhances the reliability of embedded systems, providing a robust defense against false data injection attacks and contributing to overall cybersecurity.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
