An identification method of malicious nodes in wireless communication based on dynamic reputation algorithm

被引：0
作者：
Chen J. [1 ]
机构：
[1] Puyang Vocational and Technical College, Henan, Puyang
来源：
International Journal of Information and Communication Technology | 2021年 / 19卷 / 04期
关键词：
Dynamic reputation algorithm; Identification; Malicious nodes in wireless communication;
D O I：
10.1504/IJICT.2021.118573
中图分类号：
学科分类号：
摘要：
Due to the complex internal structure of wireless communication network, the traditional methods for malicious node identification are relatively single, which leads to a large number of security risks in the network environment. This paper proposes a method of identifying malicious nodes in wireless communication based on dynamic reputation algorithm. A model of WSN wireless communication malicious node identification based on routing protocol reputation mechanism is established. The network is divided into clusters to determine the transmission path of network packets. Send the packet to the sink node and analyse it, calculate the node number and reputation value in the packet and compare with the threshold value to realise the identification of malicious nodes in wireless communication. The simulation results show that the proposed method can complete the identification of malicious nodes in wireless communication with high accuracy, and it takes less time and has better recognition performance. Copyright © 2021 Inderscience Enterprises Ltd.
引用
收藏
页码：343 / 355
页数：12
相关论文
共 23 条
  • [1] Cao Y.L., Wang X.M., He Z.B., Optimal security strategy for Malware propagation in mobile wireless sensor networks, Acta Electronica Sinica, 44, 8, pp. 1851-1857, (2016)
  • [2] Chen S.H., Fan X.G., Zhuo Z.F., Huang J.K., Sun X.M., Ad hoc secure routing protocol based on optimized trust evaluation, Journal of Beijing University of Aeronautics and Astronautics, 42, 11, pp. 2524-2531, (2016)
  • [3] Chen Y., Wei Z., Yu Z.T., Huang S.W., Preliminary study on anomaly detection of information network based on the correlation of coupling dynamic discrete event chains, Automation of Electric Power Systems, 40, 17, pp. 38-43, (2016)
  • [4] Feng H.F., Bai F.S., Xu Y.J., Urban traffic perception and critical node identification of road network based on trajectory big data, Journal of Transportation Systems Engineering and Information Technology, 30, 11, pp. 40-45, (2018)
  • [5] Guo Y., Wang L.J., Liu S., Shared key-based mutual authentication and key agreement schemes in wireless sensor networks, China Science and Technology Paper, 11, 8, pp. 865-868, (2016)
  • [6] Hou L.Q., Zhang J.M., Jin N.Z., Li Y., Zhu M., Zhang L., Network attack detection and forensics design for substation process layer and SMV secure transmission, Power System Automation, 40, 17, pp. 87-92, (2016)
  • [7] Hu F., Wei X.L., Fan J.H., Sun Q., Interference graph based interference detection algorithm, Computer Engineering, 44, 4, pp. 161-167, (2018)
  • [8] Li J., Guo X., Sun J., DDoS attack detection for industrial control systems based on extended Kalman filtering, Application of Electronic Technology, 42, 4, pp. 73-77, (2016)
  • [9] Li Y., Huang G.Q., Cao L.X., Zhang B., Li Y., Huang G., Et al., Probability controllability of complex attack networks, Computer Science and Exploration, 10, 10, pp. 1407-1419, (2016)
  • [10] Liu Y., Fang Y., Sun H., Liu T., Smart grid attack detection method based on physics-information fuzzy reasoning, China Science and Technology Paper, 11, 14, pp. 1619-1625, (2016)
123