Edge computing clone node recognition system based on machine learning (original) (raw)

Abstract

Edge computing is an important cornerstone for the construction of 5G networks, but with the development of Internet technology, the computer nodes are extremely vulnerable in attacks, especially clone attacks, causing casualties. The principle of clonal node attack is that the attacker captures the legitimate nodes in the network and obtains all their legitimate information, copies several nodes with the same ID and key information, and puts these clonal nodes in different locations in the network to attack the edge computing devices, resulting in network paralysis. How to quickly and efficiently identify clone nodes and isolate them becomes the key to prevent clone node attacks and improve the security of edge computing. In order to improve the degree of protection of edge computing and identify clonal nodes more quickly and accurately, based on edge computing of machine learning, this paper uses case analysis method, the literature analysis method, and other methods to collect data from the database, and uses parallel algorithm to build a model of clonal node recognition. The results show that the edge computing based on machine learning can greatly improve the efficiency of clonal node recognition, the recognition speed is more than 30% faster than the traditional edge computing, and the recognition accuracy reaches 0.852, which is about 50% higher than the traditional recognition. The results show that the edge computing clonal node method based on machine learning can improve the detection success rate of clonal nodes and reduce the energy consumption and transmission overhead of nodes, which is of great significance to the detection of clonal nodes.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. He W, Shiqiang Li, Huanan Yu, Jian Z, (2020) Distribution network power quality data compression storage method based on distributed compressed sensing and edge computing. Trans China Electrotechn Soc 35(21):135–146
    Google Scholar
  2. Z Peng, Jincheng Xu, Yang B (2020) Cross-domain computing resource allocation and task offloading based on edge computing in the industrial internet of things. J Internet Things 004(002):96–104
    Google Scholar
  3. Jie C, Hong W, Wenjing H et al (2018) Edge computing clone node identification method based on neural network. Commun Technol 051(010):2449–2454
    Google Scholar
  4. Xiyuan Li, Haitao J, Zhiming L (2019) Disaster recovery and management architecture of edge computing nodes. Telecommun Sci 35(S2):279–282
    Google Scholar
  5. Tong Hu, Zhijin Q, Suiping Qi et al (2018) Self-matching method of ocean observation elements based on edge computing. Ocean Technol 037(004):29–36
    Google Scholar
  6. Dong Li (2018) Edge computing system architecture and key technologies. Autom Expo 35(4):56–57
    Google Scholar
  7. Yanqiao Lu, Cuiying S, Hongwei C et al (2020) Foreign body detection method for power transmission equipment based on edge computing and deep learning. China Electric Power 053(006):27–33
    Google Scholar
  8. Limin Z (2020) Application of smart new energy——solar electric bicycle power pile system based on edge computing. Inf Constr 263(08):58–61
    Google Scholar
  9. Yueru S, Jun Li (2020) Mobile edge computing offload strategy for multi-base station and multi-user scenarios. Front Data Comp Develop 5(03):130–140
    Google Scholar
  10. Ying S, Chang Li (2020) Visual analysis of enterprise electricity consumption behavior based on edge computing gateway. Electr Appl Energy Effic Manag Technol 592(07):89–94
    Google Scholar
  11. Haochen H, Yutong Li, Tonghe W, Zhaoming Q, Junwei C (2020) A control strategy for energy Internet edge computing system based on mixed stochastic H_2/H_∞ method. Proc Chin Soc Electr Eng 656(21):115–125
    Google Scholar
  12. Jianmin Z, Yang F, Zhouyun Wu et al (2019) Multi-access edge computing (MEC) and key technologies. Telecommun Sci 35(03):160–160
    Google Scholar
  13. Zhu R, Liu L, Song H et al (2020) Multi-access edge computing enabled internet of things: advances and novel applications. Neural Comput Applic 32:15313–15316
    Article Google Scholar
  14. Tang Y, Elhoseny M (2019) Computer network security evaluation simulation model based on neural network. J Intell Fuzzy Syst 37(78):1–8
    Google Scholar
  15. Zhu Fang, Lu Ping, Jilong L, Ke Li, Bingtao H (2020) The surreal experience of home intelligence——distributed real-time rendering at the edge. Artif Intell 18(05):41–48
    Google Scholar
  16. Fajjari I, Tobagi F, Takahashi Y (2018) Cloud edge computing in the IoT. Annal Telecommun - annales des télécommunications 73(7–8):413–414
    Article Google Scholar
  17. Li X, Wan J (2018) Proactive caching for edge computing-enabled industrial mobile wireless networks. Future Gener Comp Syst 89:89–97. https://doi.org/10.1016/j.future.2018.06.017
    Article Google Scholar
  18. Bo G, Yapeng C, Haijun L et al (2018) A distributed and context-aware task assignment mechanism for collaborative mobile edge computing. Sensors 18(8):2423–2427
    Article Google Scholar
  19. Sun H, Zhou F, Hu RQ (2019) Joint offloading and computation energy efficiency maximization in a mobile edge computing system. IEEE Trans Veh Technol 68(3):3052–3056
    Google Scholar
  20. Wang H, Zeng M, Xiong Z et al (2017) Finding main causes of elevator accidents via multi-dimensional association rule in edge computing environment. China Commun (English Edition) 14(011):39–47
    Google Scholar
  21. Xingpo M, Junbin L, Renping L et al (2018) A survey on data storage and information discovery in the wsans-based edge computing systems. Sensors 18(2):546–548
    Article Google Scholar
  22. Shrestha B, Lin H (2020) Data-centric edge computing to defend power grids against IoT-based attacks. Computer 53(5):35–43
    Article Google Scholar
  23. Brian M, Christina E, Diego C et al (2018) Multiple, independent t cell lymphomas arising in an experimentally FIV-infected cat during the terminal stage of infection. Viruses 10(6):280–285
    Article Google Scholar
  24. Zhang X, Lu J, Li D (2021) Confidential information protection method of commercial information physical system based on edge computing. Neural Comput Applic 33:897–907
    Article Google Scholar
  25. Xu Z, Zhou Q (2020) Special issue on multi-modal information learning and analytics for smart city. J Ambient Intell Humaniz Comput 11(9):3471–3472
    Article Google Scholar
  26. Ansari MH, Vakili VT (2017) Detection of clone node attack in mobile wireless sensor network with optimised cost function. Int J Sensor Netw 24(3):149
    Article Google Scholar
  27. Yeh JY, Chen CH (2020) A machine learning approach to predict the success of crowdfunding fintech project. J Enterp Inf Manag. https://doi.org/10.1108/JEIM-01-2019-0017
    Article Google Scholar
  28. Harini P (2017) On node reproduction attack in wireless sensor networks. Int J Eng Technol 7(1–2):27–30
    Article Google Scholar
  29. Singh Y, Mohindru V (2018) Node authentication algorithm for securing static wireless sensor networks from node clone attack. Int J Inf Comput Secur 10(23):129–132
    Google Scholar
  30. Wang Q, Lu P (2019) Research on application of artificial intelligence in computer network technology. Int J Pattern Recognit Artif Intell 33(5):1959015
    Article Google Scholar

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2020JJ4757).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, Hunan, 410000, China
    Xiang Xiao & Ming Zhao

Authors

  1. Xiang Xiao
  2. Ming Zhao

Corresponding author

Correspondence toMing Zhao.

Ethics declarations

Conflict of interest

There is no potential conflict of interest in our paper, and all authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

About this article

Cite this article

Xiao, X., Zhao, M. Edge computing clone node recognition system based on machine learning.Neural Comput & Applic 34, 9289–9300 (2022). https://doi.org/10.1007/s00521-021-06283-1

Download citation

Keywords