Malicious Node Detection in Wireless Sensor Networks Using an Autoregression Technique

Curiae, D.-l.; Baniaș, Ovidiu; Drăgan, Florin; Voloşencu, Constantin; Dranga, O.

doi:10.1109/icns.2007.79

Cited by 60 publications

(28 citation statements)

References 14 publications

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“…The work reported in Curiac et al (2007) is the closest to our approach. They proposed to detect a malicious node by comparing its output with an aggregation value.…”

Section: Related Workmentioning

confidence: 97%

Weighted trust evaluation-based malicious node detection for wireless sensor networks

Chen

et al. 2009

IJICS

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Deployed in a hostile environment, the individual Sensor Node (SN) of a Wireless Sensor Network (WSN) could be easily compromised by an adversary due to constraints such as limited memory space and computing capability. Therefore, it is critical to detect and isolate compromised nodes in order to avoid being misled by the falsified information injected by adversaries through compromised nodes. However, it is challenging to secure the flat topology networks effectively because of the poor scalability and high communication overhead. On top of a hierarchical WSN architecture, a novel algorithm based on Weighted Trust Evaluation (WTE) to detect malicious nodes for hierarchical sensor networks is proposed in this paper. The hierarchical network can reduce the communication overhead among SNs by utilising clustered topology. The proposed algorithm models a cluster of SNs and detects malicious nodes by examining their weights that represent the reliability of SNs. Through intensive simulations, the accuracy and effectiveness of the proposed detection algorithm are verified.Keywords: wireless sensor networks; WSNs; network security; hierarchical topology; malicious node detection.Copyright © 2009 Inderscience Enterprises Ltd. Weighted trust evaluation-based malicious node detection for WSNs 133Reference to this paper should be made as follows: Hu, H., Chen, Y., Ku, W-S., Su, Z. and Chen, C-H.J. (2009) 'Weighted trust evaluation-based malicious node detection for wireless sensor networks', Int.

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“…The work reported in Curiac et al (2007) is the closest to our approach. They proposed to detect a malicious node by comparing its output with an aggregation value.…”

Section: Related Workmentioning

confidence: 97%

Weighted trust evaluation-based malicious node detection for wireless sensor networks

Chen

et al. 2009

IJICS

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“…We evaluated our approach for outlier detection by comparing with MBOD [3] and AROD [6] on data sets from [1]. Figure 3 (a) shows the outliers detected by the three methods (MBOD, AROD and KDE-Track) when applied on the dew point data set from [1] from January to June, 2011, with more than 260K data points.…”

Section: Density-based Outlier Detectionmentioning

confidence: 99%

“…An auto-regression based outlier detection (AROD) proposed in [6] identifies malicious nodes in wireless sensor network by building an AR-model to predict the value of the new incoming data based on the latest set of observed data samples. If the difference between the predicted and the actual values is greater than a predefined threshold then the point is declared as an outlier.…”

Section: Density-based Outlier Detectionmentioning

confidence: 99%

Efficient estimation of dynamic density functions with an application to outlier detection

Qahtan

Zhang

Wang

2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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“…In [16][17][18] researchers were primarily concerned with identifying suspicious nodes. In [19], Curiac et al proposed detecting a malicious node by comparing its output with its estimated value computed by an autoregressive predictor. In [20], Du et al proposed a scheme to detect malicious attacks in localizations.…”

Section: Related Workmentioning

confidence: 99%

The impact of disabling suspicious node communications on network lifetime in wireless ad hoc sensor networks

Pala¹,

Inanc²

2016

Turk J Elec Eng & Comp Sci

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Abstract:In wireless sensor networks (WSNs), the data observed by different nodes must be relayed safely to the base station over intermediate nodes. In the network environment, some sensor nodes can act suspiciously when they enter someone else's control or due to other equipment failure. Data packets that are sent through suspicious nodes may be randomly dropped or may be not delivered as desired. In this paper, we investigate the impact of disabling suspicious nodes communications on network lifetime through a linear programming framework. We build a mathematical programming framework and perform comprehensive numerical analysis. Our results show that the decrease in WSN lifetime is less than 8.0% if the number of suspicious nodes is not higher than 10%.

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Malicious Node Detection in Wireless Sensor Networks Using an Autoregression Technique

Cited by 60 publications

References 14 publications

Weighted trust evaluation-based malicious node detection for wireless sensor networks

Weighted trust evaluation-based malicious node detection for wireless sensor networks

Efficient estimation of dynamic density functions with an application to outlier detection

The impact of disabling suspicious node communications on network lifetime in wireless ad hoc sensor networks

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