Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems
Guardat en:
| Publicat a: | Machines vol. 12, no. 9 (2024), p. 664 |
|---|---|
| Autor principal: | |
| Altres autors: | , , |
| Publicat: |
MDPI AG
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3110573202 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2075-1702 | ||
| 024 | 7 | |a 10.3390/machines12090664 |2 doi | |
| 035 | |a 3110573202 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231531 |2 nlm | ||
| 100 | 1 | |a Pacheco, Jesus | |
| 245 | 1 | |a Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Microcontrollers | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a System reliability | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Signal processing | ||
| 653 | |a Discrete Wavelet Transform | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Wavelet analysis | ||
| 653 | |a Internet of Things | ||
| 653 | |a Bias | ||
| 653 | |a Machine learning | ||
| 653 | |a Data integrity | ||
| 653 | |a Embedded systems | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Neural networks | ||
| 653 | |a Sensors | ||
| 653 | |a Support vector machines | ||
| 653 | |a Classification | ||
| 653 | |a Intelligence | ||
| 653 | |a Methods | ||
| 653 | |a Anomalies | ||
| 653 | |a Real time | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Nuclear power plants | ||
| 700 | 1 | |a Benitez, Victor H | |
| 700 | 1 | |a Pérez, Guillermo | |
| 700 | 1 | |a Brau, Agustín | |
| 773 | 0 | |t Machines |g vol. 12, no. 9 (2024), p. 664 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3110573202/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3110573202/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3110573202/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |