Abnormal-Node detection based on spatio-Temporal and Multivariate-Attribute correlation in wireless sensor networks

Nesrine Berjab; Hieu Hanh Le; Chia Mu Yu; Sy Yen Kuo; Haruo Yokota

doi:10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00106

Abnormal-Node detection based on spatio-Temporal and Multivariate-Attribute correlation in wireless sensor networks

Nesrine Berjab, Hieu Hanh Le, Chia Mu Yu, Sy Yen Kuo, Haruo Yokota

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

In wireless sensor networks (WSNs), data can be subject to malicious attacks and failures, leading to unreliability. This vulnerability poses a challenge to environmental monitoring applications by creating false alarms. To guarantee a trustworthy system, we therefore need to detect abnormal nodes. In this paper, we propose a new framework for detecting abnormal nodes in clustered heterogeneous WSNs. It makes use of observed spatiotemporal (ST) and multivariate-Attribute (MVA) sensor correlations, while considering the background knowledge of the monitored environment. Based on the ST correlations, the collected data is analyzed by computing the crosscorrelation between sensor streams. A new method is proposed for evaluating the intensity of the correlation between two sensor streams. The crosscorrelation value obtained is compared against two thresholds, the lag threshold and the correlation threshold. Based on available background knowledge and the observed MVA correlations, a number of rules are presented to detect abnormal nodes while identifying real events. Our experiments on real-world sensor data demonstrate that our approach captures the correlation and discovers abnormal nodes efficiently.

Original language	English
Title of host publication	Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	568-575
Number of pages	8
ISBN (Electronic)	9781538675182
DOIs	https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00106
State	Published - 26 10 2018
Externally published	Yes
Event	16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018 - Athens, Greece Duration: 12 08 2018 → 15 08 2018

Publication series

Name	Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018

Conference

Conference	16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
Country/Territory	Greece
City	Athens
Period	12/08/18 → 15/08/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Abnormal nodes detection
Internet of Things
Lag correlation
Security
Sensor correlation
Time series analysis
Wireless Sensor Networks

Access to Document

10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00106

Cite this

Berjab, N., Le, H. H., Yu, C. M., Kuo, S. Y., & Yokota, H. (2018). Abnormal-Node detection based on spatio-Temporal and Multivariate-Attribute correlation in wireless sensor networks. In Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018 (pp. 568-575). Article 8511949 (Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00106

Berjab, Nesrine ; Le, Hieu Hanh ; Yu, Chia Mu et al. / Abnormal-Node detection based on spatio-Temporal and Multivariate-Attribute correlation in wireless sensor networks. Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 568-575 (Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018).

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abstract = "In wireless sensor networks (WSNs), data can be subject to malicious attacks and failures, leading to unreliability. This vulnerability poses a challenge to environmental monitoring applications by creating false alarms. To guarantee a trustworthy system, we therefore need to detect abnormal nodes. In this paper, we propose a new framework for detecting abnormal nodes in clustered heterogeneous WSNs. It makes use of observed spatiotemporal (ST) and multivariate-Attribute (MVA) sensor correlations, while considering the background knowledge of the monitored environment. Based on the ST correlations, the collected data is analyzed by computing the crosscorrelation between sensor streams. A new method is proposed for evaluating the intensity of the correlation between two sensor streams. The crosscorrelation value obtained is compared against two thresholds, the lag threshold and the correlation threshold. Based on available background knowledge and the observed MVA correlations, a number of rules are presented to detect abnormal nodes while identifying real events. Our experiments on real-world sensor data demonstrate that our approach captures the correlation and discovers abnormal nodes efficiently.",

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booktitle = "Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018",

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Berjab, N, Le, HH, Yu, CM, Kuo, SY & Yokota, H 2018, Abnormal-Node detection based on spatio-Temporal and Multivariate-Attribute correlation in wireless sensor networks. in Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018., 8511949, Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018, Institute of Electrical and Electronics Engineers Inc., pp. 568-575, 16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018, Athens, Greece, 12/08/18. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00106

Abnormal-Node detection based on spatio-Temporal and Multivariate-Attribute correlation in wireless sensor networks. / Berjab, Nesrine; Le, Hieu Hanh; Yu, Chia Mu et al.
Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 568-575 8511949 (Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yokota, Haruo

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AB - In wireless sensor networks (WSNs), data can be subject to malicious attacks and failures, leading to unreliability. This vulnerability poses a challenge to environmental monitoring applications by creating false alarms. To guarantee a trustworthy system, we therefore need to detect abnormal nodes. In this paper, we propose a new framework for detecting abnormal nodes in clustered heterogeneous WSNs. It makes use of observed spatiotemporal (ST) and multivariate-Attribute (MVA) sensor correlations, while considering the background knowledge of the monitored environment. Based on the ST correlations, the collected data is analyzed by computing the crosscorrelation between sensor streams. A new method is proposed for evaluating the intensity of the correlation between two sensor streams. The crosscorrelation value obtained is compared against two thresholds, the lag threshold and the correlation threshold. Based on available background knowledge and the observed MVA correlations, a number of rules are presented to detect abnormal nodes while identifying real events. Our experiments on real-world sensor data demonstrate that our approach captures the correlation and discovers abnormal nodes efficiently.

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KW - Internet of Things

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PB - Institute of Electrical and Electronics Engineers Inc.

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Berjab N, Le HH, Yu CM, Kuo SY, Yokota H. Abnormal-Node detection based on spatio-Temporal and Multivariate-Attribute correlation in wireless sensor networks. In Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 568-575. 8511949. (Proceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018). doi: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00106

Abnormal-Node detection based on spatio-Temporal and Multivariate-Attribute correlation in wireless sensor networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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