Volume 38, Number 3, June 2020
|Page(s)||634 - 642|
|Published online||06 August 2020|
Model for Malicious Node Recognition Based on Environmental Parameter Optimization and Time Reputation Sequence
Northeast Electric Power University, Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology, Ministry of Education, Jilin 132012, China
2 School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
3 School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
4 Ericsson(Xi'an) Information and Communication Technology Service Co., Ltd. Dalian Branch, Dalian 116000, China
Wireless sensor network (WSN) works in a complex environment. To interdict the malicious nodes which attacks the safety of network, such as interrupt attacks and selective forwarding attacks, based on TS-BRS reputation model, a model for malicious node identification based on MNRT-OEP&RS algorithm is constructed. Using the linear regression of machine learning and combining the energy of nodes, data volume, number of adjacent nodes, the node sparsity and other deterministic parameters can solve environmental parameters. Then the similarity of between the benchmark reputation sequence and cycle reputation sequence sets the dynamic reputation double threshold are calculated in order to identify the malicious nodes by dynamically considering the information forwarding behavior. The simulated results show that the improved algorithm can guarantee the security of wireless sensor networks in complex environments effectively with above 90% recognition of malicious nodes and below 8% false positive rate.
Key words: wireless senor network / network security / environmental impact / linear regression / dynamic reputation double threshold
关键字 : 无线传感器网络 / 网络安全 / 环境影响 / 线性回归 / 动态信誉双阈值
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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