A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier
The application of wireless sensor networks (WSN) is increasing with the emergence of the 'Internet of Things' concept. Nonetheless, the sensed data quality and reliability are sometimes affected by factors such as sensor's faults, intrusions and unusual events among others. Consequen...
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2018
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my.utm.843062019-12-28T01:46:36Z http://eprints.utm.my/id/eprint/84306/ A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier A. Rassam, Murad Maarof, Mohd. Aizaini Zainal, Anazida QA75 Electronic computers. Computer science The application of wireless sensor networks (WSN) is increasing with the emergence of the 'Internet of Things' concept. Nonetheless, the sensed data quality and reliability are sometimes affected by factors such as sensor's faults, intrusions and unusual events among others. Consequently, the real time and effective detection mechanisms of anomalous data are necessary for reliable decisions. In this paper, we proposed a one-class principal component classifier (OCPCC) based distributed anomaly detection model for WSN, which utilises the spatial correlations among sensed data in closed neighbourhoods. The feasibility of the model was validated using real world datasets and compared with local detection and some existing detection approaches from literature. The results show that the proposed model improves the detection rate of anomalous data compared to local model. A comparison with existing distributed models reveals the advantages of the proposed model in terms of efficiency while achieving better or comparable detection effectiveness. Inderscience Enterprises Ltd. 2018 Article PeerReviewed A. Rassam, Murad and Maarof, Mohd. Aizaini and Zainal, Anazida (2018) A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier. International Journal of Sensor Networks, 27 (3). pp. 200-214. ISSN 1748-1279 https://dx.doi.org/10.1504/IJSNET.2018.093126 |
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QA75 Electronic computers. Computer science A. Rassam, Murad Maarof, Mohd. Aizaini Zainal, Anazida A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier |
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The application of wireless sensor networks (WSN) is increasing with the emergence of the 'Internet of Things' concept. Nonetheless, the sensed data quality and reliability are sometimes affected by factors such as sensor's faults, intrusions and unusual events among others. Consequently, the real time and effective detection mechanisms of anomalous data are necessary for reliable decisions. In this paper, we proposed a one-class principal component classifier (OCPCC) based distributed anomaly detection model for WSN, which utilises the spatial correlations among sensed data in closed neighbourhoods. The feasibility of the model was validated using real world datasets and compared with local detection and some existing detection approaches from literature. The results show that the proposed model improves the detection rate of anomalous data compared to local model. A comparison with existing distributed models reveals the advantages of the proposed model in terms of efficiency while achieving better or comparable detection effectiveness. |
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Article |
author |
A. Rassam, Murad Maarof, Mohd. Aizaini Zainal, Anazida |
author_facet |
A. Rassam, Murad Maarof, Mohd. Aizaini Zainal, Anazida |
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A. Rassam, Murad |
title |
A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier |
title_short |
A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier |
title_full |
A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier |
title_fullStr |
A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier |
title_full_unstemmed |
A distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier |
title_sort |
distributed anomaly detection model for wireless sensor networks based on the one-class principal component classifier |
publisher |
Inderscience Enterprises Ltd. |
publishDate |
2018 |
url |
http://eprints.utm.my/id/eprint/84306/ https://dx.doi.org/10.1504/IJSNET.2018.093126 |
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