Trust evaluation model with entropy-based weight assignment for malicious node\u2019s detection in wireless sensor networks

Xueqiang Yin,Shining Li

doi:10.1186/s13638-019-1524-z

Abstract

Trust management is considered as an effective complementary mechanism to ensure the security of sensor networks. Based on historical behavior, the trust value can be evaluated and applied to estimate the reliability of the node. For the analysis of the possible attack behavior of malicious nodes, we proposed a trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks. To mitigate the malicious attacks such as packet dropping or packet modifications, multidimensional trust indicators are derived from communication between adjacent sensor nodes, and direct and indirect trust values will be estimated based on the corresponding behaviors of those sensor nodes. In order to improve the validity of trust quantification and ensure the objectivity of evaluation, the entropy weight method is applied to determine the proper value of the weight. Finally, the indirect trust value and direct trust value are synthesized to obtain the overall trust. Experimental results show that the proposed scheme performs well in terms of the identification of malicious node.

Highlights

Nowadays, wireless sensor networks (WSNs) have become one of the most useful technologies and attracted more and more attention from researchers [1]
5 Experimental results In order to verify the validity of the proposed trust model for wireless sensor networks, simulation experiments are conducted
6 Conclusions In this paper, we proposed a trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks

Summary

Introduction

Wireless sensor networks (WSNs) have become one of the most useful technologies and attracted more and more attention from researchers [1]. In the centralized trust evaluation model, the center obtains global information such as exchange records between sensor nodes or user’s feedback and calculates trust according to a certain rule. In [17], Jiang et al proposed an efficient distributed trust model according to the exchange messages from all sensor nodes, and the trust metrics include communication overhead, energy consumption, and data validity. Based on the hierarchical network structure, Liao et al [21] proposed a weighted trust evaluation strategy, which updates the weighted trust value continuously by comparing the data collected by sensor nodes and the final data fusion results. To achieve the tradeoff between energy conservation and network security, Liao et al [21] presented a mixed and continuous monitor-forward model based on game theory to mitigate the selective-forwarding attack, in which the monitoring node conducts a strategy continuously to determine the duration of behavior surveillance. If the fusion results exceed the threshold value, it demonstrates that the cluster head is possible to be compromised nodes and to be added to the blacklist

Trust model

Clustering objective function

Indirect trust

Conclusions