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  • Zhao, H. (författare)

An enhanced intrusion detection method for AIM of smart grid

  • Artikel/kapitelEngelska2023

Förlag, utgivningsår, omfång ...

  • 2023-02-03
  • Springer Science and Business Media Deutschland GmbH,2023
  • printrdacarrier

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  • LIBRIS-ID:oai:DiVA.org:uu-500256
  • https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-500256URI
  • https://doi.org/10.1007/s12652-023-04538-4DOI

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  • Språk:engelska
  • Sammanfattning på:engelska

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  • Export Date: 13 April 2023; Article; 通讯地址: Qiao, S.; School of Computer Science and Technology, West Changjiang Road, Shandong Province, China; 电子邮件: siboqiao@126.com; 基金资助详情: Major Scientific and Technological Innovation Project of Shandong Province, 2019JZZY01013; 基金资助文本 1: Thanks to Prof. Huiqi Zhao for project funding support Major Scientific and Technological Innovation Projects of Shandong Province, China, No. 2019JZZY01013.; 参考文献: Al-Hawawreh, M., Sitnikova, E., Aboutorab, N., X-IIoTID: a connectivity-and device-agnostic intrusion dataset for industrial internet of things (2021) IEEE Internet Things J; Breiman, L., Bagging predictors (1996) Mach Learn, 24 (2), pp. 123-140; Breiman, L.E.O., Random forests (2001) Mach Learn, pp. 5-32; Chang, J.F., Chu, S.C., Roddick, J.F., Pan, J.S., A parallel particle swarm optimization algorithm with communication strategies (2005) J Inf Sci Eng, 21 (4), pp. 809-818; Chen, T.Y., Chi, T.M., On the improvements of the particle swarm optimization algorithm (2010) Adv Eng Softw, 41 (2), pp. 229-239; 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  • As a highly automated power transmission network, the smart grid can monitor each user and grid node and connect different devices to improve the function of conventional power network significantly, but this heterogeneous network also brings greater security risks, attackers can use vulnerabilities existing in smart grids. Intrusion Detection System (IDS) constitutes an important means to protect critical information from being leaked. in a smart grid environment. In this paper, we proposed an AMI intrusion detection model for smart grid, which is widely distributed in the three-layer architecture of the grid system through particle swarm algorithm combined with random forest method. To improve the model’s accuracy, this paper adopts the dynamic weight formula and various adaptive mutation methods to optimize the iterative process of the algorithm. Besides, we use parallel strategy to make up for the lack of precision in the mutation of the algorithm. The AM-PPSO algorithm proposed in this paper performs well in the CEC2017 benchmark function test, effectively ensuring the improvement of the RF classifier. Finally, we use NPL-KDD, UNSW-UB15, and X-IIoTID standard intrusion detection datasets to simulate, results show that our model achieves 97–99% classification of the three datasets. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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  • Liu, G. (författare)
  • Sun, H. (författare)
  • Zhong, G. (författare)
  • Pang, S. (författare)
  • Qiao, S. (författare)
  • Lv, Z. (författare)

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  • Ingår i:Journal of Ambient Intelligence and Humanized Computing: Springer Science and Business Media Deutschland GmbH1868-51371868-5145

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