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论文:2020,Vol:38,Issue(3):634-642 |
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引用本文： |
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滕志军, 庞宝贺, 孙铭阳, 谢露莹, 郭力文. 基于环境参数优化和时间信誉序列的恶意节点识别模型[J]. 西北工业大学学报 |
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TENG Zhijun, PANG Baohe, SUN Mingyang, XIE Luying, GUO Liwen. Model for Malicious Node Recognition Based on Environmental Parameter Optimization and Time Reputation Sequence[J]. Northwestern polytechnical university |
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| 基于环境参数优化和时间信誉序列的恶意节点识别模型 |
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| 滕志军1,2, 庞宝贺2, 孙铭阳3, 谢露莹2, 郭力文4 |
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1. 东北电力大学 现代电力系统仿真控制与绿色电能新技术教育部重点实验室, 吉林 吉林 132012;
2. 东北电力大学 电气工程学院, 长春 吉林 132012;
3. 东北电力大学 自动化工程学院, 吉林 吉林 132012;
4. 爱立信(西安)信息通信技术服务有限公司大连分公司, 辽宁 大连 116000 |
| 摘要: |
| 在复杂无线传感器网络环境中，为阻断恶意节点发动危及网络安全的中断攻击和选择性转发攻击，在TS-BRS信誉模型的基础上，搭建基于MNRT-OEP&RS的恶意节点识别模型，利用机器学习中的线性回归并结合节点能量、工作量、邻节点数量、节点疏松度等可确定参数求解环境参数，计算基准信誉序列与周期内的节点信誉序列的相似度；设定动态信誉双阈值，对节点的信息转发行为进行动态考量，以甄别恶意节点。仿真实验表明，改进后的算法对恶意节点的识别率可达90%以上，对正常节点误判率降低到8%以下，有效提高复杂环境下无线传感器网络的安全性。 |
| 关键词:
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
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| Model for Malicious Node Recognition Based on Environmental Parameter Optimization and Time Reputation Sequence |
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| TENG Zhijun1,2, PANG Baohe2, SUN Mingyang3, XIE Luying2, GUO Liwen4 |
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1. 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 |
| Abstract: |
| 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
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| 收稿日期: 2019-07-08
修回日期:
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| DOI: 10.1051/jnwpu/20203830634 |
| 基金项目: 国家自然科学基金青年科学基金项目（61501107）及吉林省教育厅"十三五"科学研究规划项目（JJKH20180439KJ）资助 |
| 通讯作者:
Email： |
| 作者简介: 滕志军(1973-),东北电力大学教授,主要从事无线通信技术与物联网网络安全研究。
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