| 引用本文: | 滕志军,谷金亮,崔瑶瑶,朱思安,庞宝贺.WSN中计及信誉度的人工蜂群恶意节点识别策略[J].哈尔滨工业大学学报,2026,58(3):181.DOI:10.11918/202305018 |
| TENG Zhijun,GU Jinliang,CUI Yaoyao,ZHU Si’an,PANG Baohe.Malicious node identification strategy based on artificial bee colony considering reputation in WSN[J].Journal of Harbin Institute of Technology,2026,58(3):181.DOI:10.11918/202305018 |
|
| |
|
|
| 本文已被：浏览 152次 下载 15次 |
码上扫一扫！ |
|
|
| WSN中计及信誉度的人工蜂群恶意节点识别策略 |
|
滕志军1,2,谷金亮2,崔瑶瑶2,朱思安2,庞宝贺3
|
|
(1.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(东北电力大学),吉林 吉林 132012; 2.东北电力大学 电气工程学院,吉林 吉林 132012; 3.哈尔滨工程大学 信息与通信工程学院,哈尔滨 150000)
|
|
| 摘要: |
| 在无线传感器网络（WSN）复杂的应用环境中,为抵御恶意节点发起的选择性转发攻击和不诚实建议攻击、提高网络安全性能,在人工蜂群（ABC）算法的基础上,提出一种计及信誉度的人工蜂群无线传感器网络恶意节点识别策略（CR-ABC）。借助模糊信任模型,融合模糊综合评价机制,综合通信特征、数据属性、物理属性3个方面的影响因素计算节点综合信任值,提高信誉模型的检测精度；引入建议偏差值函数和交互指数偏差值函数,利用ABC算法优化模糊信任模型,确保在恶意节点数量较多时,系统仍保持较高的识别率和较低的误判率。仿真结果表明,CR-ABC对选择性转发攻击的识别率可达90%以上,对正常节点误判率低于6%；对于不诚实建议攻击,即使不诚实节点的数量占比达到50%,CR-ABC仍能保持优异的识别性能,可有效提高复杂环境下WSN的安全性和可靠性。 |
| 关键词: 无线传感器网络 信誉度 人工蜂群算法 选择性转发攻击 不诚实建议攻击 |
| DOI：10.11918/202305018 |
| 分类号:TN92 |
| 基金项目:国家自然科学基金青年科学基金项目(61501107) |
|
| Malicious node identification strategy based on artificial bee colony considering reputation in WSN |
|
TENG Zhijun1,2,GU Jinliang2,CUI Yaoyao2,ZHU Si’an2,PANG Baohe3
|
|
(1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin 132012, Jilin,China; 2. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, Jilin, China; 3. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150000, China)
|
| Abstract: |
| In the complex application environment of wireless sensor networks (WSN), in order to resist the selective forwarding attack and dishonest recommendation attack launched by malicious nodes and improve the safety performance of the network, this paper proposes a malicious node identification strategy based on artificial bee colony (ABC) considering reputation (CR-ABC) in WSN. By utilizing a fuzzy trust model (FTM) and integrating a fuzzy comprehensive evaluation mechanism, the paper calculates the comprehensive trust value of nodes based on three influencing factors: communication features, data attributes, and physical attributes to improve the detection accuracy of the reputation model. The paper introduces the suggested deviation function and the interaction index deviation function and uses the ABC algorithm to optimize the FTM, aiming to ensure that the system still maintains a higher identification rate and a lower misjudgment rate when there are too many malicious nodes. The simulation results show that the identification rate of CR-ABC for selective forwarding attacks can reach over 90%, and the misjudgment rate for normal nodes can be reduced to less than 6%. For dishonest recommendation attacks, even if the number of dishonest nodes reaches 50%, CR-ABC still maintains a high identification performance, which can effectively improve the security and reliability of WSN in complex environments. |
| Key words: wireless sensor network reputation artificial bee colony algorithm selective forwarding attack dishonest recommendation attack |
|
|
|
|
