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Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine
Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measureme...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659524/ https://www.ncbi.nlm.nih.gov/pubmed/34884022 http://dx.doi.org/10.3390/s21238017 |
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author | Zamry, Nurfazrina M. Zainal, Anazida Rassam, Murad A. Alkhammash, Eman H. Ghaleb, Fuad A. Saeed, Faisal |
author_facet | Zamry, Nurfazrina M. Zainal, Anazida Rassam, Murad A. Alkhammash, Eman H. Ghaleb, Fuad A. Saeed, Faisal |
author_sort | Zamry, Nurfazrina M. |
collection | PubMed |
description | Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with [Formula: see text] (nd) memory utilization and no communication overhead. |
format | Online Article Text |
id | pubmed-8659524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86595242021-12-10 Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine Zamry, Nurfazrina M. Zainal, Anazida Rassam, Murad A. Alkhammash, Eman H. Ghaleb, Fuad A. Saeed, Faisal Sensors (Basel) Article Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with [Formula: see text] (nd) memory utilization and no communication overhead. MDPI 2021-11-30 /pmc/articles/PMC8659524/ /pubmed/34884022 http://dx.doi.org/10.3390/s21238017 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zamry, Nurfazrina M. Zainal, Anazida Rassam, Murad A. Alkhammash, Eman H. Ghaleb, Fuad A. Saeed, Faisal Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine |
title | Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine |
title_full | Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine |
title_fullStr | Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine |
title_full_unstemmed | Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine |
title_short | Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine |
title_sort | lightweight anomaly detection scheme using incremental principal component analysis and support vector machine |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659524/ https://www.ncbi.nlm.nih.gov/pubmed/34884022 http://dx.doi.org/10.3390/s21238017 |
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