The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les nœuds malveillants sans surveillance constituent de grandes menaces de sécurité pour l’intégrité des réseaux de capteurs IoT. Cependant, des mesures de prévention telles que la cryptographie et l'authentification sont difficiles à déployer dans des nœuds de capteurs IoT aux ressources limitées, avec de faibles capacités de traitement et une alimentation électrique courte. Pour lutter contre ces nœuds de capteurs malveillants, dans cette étude, la méthode de calcul de confiance est appliquée aux réseaux de capteurs IoT en tant que mécanisme de sécurité léger, et basée sur la théorie des polynômes de Chebyshev pour l'approximation des séries temporelles, la séquence de données de confiance générée par chaque nœud de capteur est linéarisé et traité comme une série temporelle pour la détection des nœuds malveillants. La méthode proposée est évaluée par rapport à des schémas existants à l'aide de plusieurs simulations et les résultats démontrent que notre méthode peut mieux gérer les nœuds malveillants, ce qui entraîne un taux de livraison de paquets corrects plus élevé.
Fang WANG
Civil Aviation Flight University of China
Zhe WEI
Civil Aviation Flight University of China
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Fang WANG, Zhe WEI, "A Statistical Trust for Detecting Malicious Nodes in IoT Sensor Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 8, pp. 1084-1087, August 2021, doi: 10.1587/transfun.2020EAL2125.
Abstract: The unattended malicious nodes pose great security threats to the integrity of the IoT sensor networks. However, preventions such as cryptography and authentication are difficult to be deployed in resource constrained IoT sensor nodes with low processing capabilities and short power supply. To tackle these malicious sensor nodes, in this study, the trust computing method is applied into the IoT sensor networks as a light weight security mechanism, and based on the theory of Chebyshev Polynomials for the approximation of time series, the trust data sequence generated by each sensor node is linearized and treated as a time series for malicious node detection. The proposed method is evaluated against existing schemes using several simulations and the results demonstrate that our method can better deal with malicious nodes resulting in higher correct packet delivery rate.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAL2125/_p
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@ARTICLE{e104-a_8_1084,
author={Fang WANG, Zhe WEI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Statistical Trust for Detecting Malicious Nodes in IoT Sensor Networks},
year={2021},
volume={E104-A},
number={8},
pages={1084-1087},
abstract={The unattended malicious nodes pose great security threats to the integrity of the IoT sensor networks. However, preventions such as cryptography and authentication are difficult to be deployed in resource constrained IoT sensor nodes with low processing capabilities and short power supply. To tackle these malicious sensor nodes, in this study, the trust computing method is applied into the IoT sensor networks as a light weight security mechanism, and based on the theory of Chebyshev Polynomials for the approximation of time series, the trust data sequence generated by each sensor node is linearized and treated as a time series for malicious node detection. The proposed method is evaluated against existing schemes using several simulations and the results demonstrate that our method can better deal with malicious nodes resulting in higher correct packet delivery rate.},
keywords={},
doi={10.1587/transfun.2020EAL2125},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - A Statistical Trust for Detecting Malicious Nodes in IoT Sensor Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1084
EP - 1087
AU - Fang WANG
AU - Zhe WEI
PY - 2021
DO - 10.1587/transfun.2020EAL2125
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2021
AB - The unattended malicious nodes pose great security threats to the integrity of the IoT sensor networks. However, preventions such as cryptography and authentication are difficult to be deployed in resource constrained IoT sensor nodes with low processing capabilities and short power supply. To tackle these malicious sensor nodes, in this study, the trust computing method is applied into the IoT sensor networks as a light weight security mechanism, and based on the theory of Chebyshev Polynomials for the approximation of time series, the trust data sequence generated by each sensor node is linearized and treated as a time series for malicious node detection. The proposed method is evaluated against existing schemes using several simulations and the results demonstrate that our method can better deal with malicious nodes resulting in higher correct packet delivery rate.
ER -