IEICE TRANSACTIONS on Fundamentals
Countering Malicious Nodes of Inconsistent Behaviors in WSNs: A Combined Approach of Statistic Reputation and Time Series
Summary :
In wireless sensor networks, or WSNs, a malicious node is able to cover itself by switching between good and bad behaviors. Even when running under a reputation mechanism, such a node can still behave maliciously now and then so long as its reputation is within the acceptable level. To address this inconsistent behavior issue, a combined approach of statistic reputation and time series is proposed in this study, in which the negative binomial reputation is applied to rate the nodes' reputation and concept of time series is borrowed to analyze the reputation results. Simulations show that the proposed method can effectively counter inconsistent behavior nodes and thus improves the overall system performance.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.7 pp.1584-1587
- Publication Date
- 2015/07/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.1584
- Type of Manuscript
- LETTER
- Category
- Mobile Information Network and Personal Communications
Authors
Keyword
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Fang WANG, Zhe WEI, "Countering Malicious Nodes of Inconsistent Behaviors in WSNs: A Combined Approach of Statistic Reputation and Time Series" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 7, pp. 1584-1587, July 2015, doi: 10.1587/transfun.E98.A.1584.
Abstract: In wireless sensor networks, or WSNs, a malicious node is able to cover itself by switching between good and bad behaviors. Even when running under a reputation mechanism, such a node can still behave maliciously now and then so long as its reputation is within the acceptable level. To address this inconsistent behavior issue, a combined approach of statistic reputation and time series is proposed in this study, in which the negative binomial reputation is applied to rate the nodes' reputation and concept of time series is borrowed to analyze the reputation results. Simulations show that the proposed method can effectively counter inconsistent behavior nodes and thus improves the overall system performance.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.1584/_p
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@ARTICLE{e98-a_7_1584,
author={Fang WANG, Zhe WEI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Countering Malicious Nodes of Inconsistent Behaviors in WSNs: A Combined Approach of Statistic Reputation and Time Series},
year={2015},
volume={E98-A},
number={7},
pages={1584-1587},
abstract={In wireless sensor networks, or WSNs, a malicious node is able to cover itself by switching between good and bad behaviors. Even when running under a reputation mechanism, such a node can still behave maliciously now and then so long as its reputation is within the acceptable level. To address this inconsistent behavior issue, a combined approach of statistic reputation and time series is proposed in this study, in which the negative binomial reputation is applied to rate the nodes' reputation and concept of time series is borrowed to analyze the reputation results. Simulations show that the proposed method can effectively counter inconsistent behavior nodes and thus improves the overall system performance.},
keywords={},
doi={10.1587/transfun.E98.A.1584},
ISSN={1745-1337},
month={July},}
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TY - JOUR
TI - Countering Malicious Nodes of Inconsistent Behaviors in WSNs: A Combined Approach of Statistic Reputation and Time Series
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1584
EP - 1587
AU - Fang WANG
AU - Zhe WEI
PY - 2015
DO - 10.1587/transfun.E98.A.1584
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2015
AB - In wireless sensor networks, or WSNs, a malicious node is able to cover itself by switching between good and bad behaviors. Even when running under a reputation mechanism, such a node can still behave maliciously now and then so long as its reputation is within the acceptable level. To address this inconsistent behavior issue, a combined approach of statistic reputation and time series is proposed in this study, in which the negative binomial reputation is applied to rate the nodes' reputation and concept of time series is borrowed to analyze the reputation results. Simulations show that the proposed method can effectively counter inconsistent behavior nodes and thus improves the overall system performance.
ER -
English
