Technology of Information Security
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 1
272-277

DDoS in V2G: intrusion detection based on federated learning and CNN-BiLSTM

Lin Zhaoliang
Li Jinguo
Huang Runke
School of Computer Science & Technology, Shanghai University of Electric Power, Shanghai 201306, China
DOI: 10.19734/j.issn.1001-3695.2022.06.0265

Abstract

DDoS attack is one of the major threats to V2G networks, which can deplete the communication resources of servers in a short period of time. In addition, the previous approach is based on the centralized model, it transfers data from the edge devices to the central server for training, which may expose the data to various attacks. Therefore, this paper investigated a federated learning-based intrusion detection system. Firstly, considering the high dimensionality of the V2G network data and the temporal dependence among the data, it reduced the collected data in dimensionality by the improved feature selection algorithm to reduce the redundant features, and then it passed the processed data into the hybrid model incorporating the CNN and the bi-directional long short-term memory network to capture the temporal dependence in the data, and it introduced the batch normalization to prevent the gradient disappearance during the training of the neural network. Besides, to prevent privacy leakage, it allowed the data to remain local for the training of the neural network model in conjunction with the inherent nature of federated learning. In addition, to solve the problem of excessive network load pressure caused by federated learning communication, it designed a scheme to reduce the network load pressure by setting a dynamic communication threshold to filter the optimal edge devices involved in the optimization of the update. The experimental results show that the accuracy of the method can be as high as 99.95% and the communication time of a single round is reduced by 1.7 s.

Foundation Support

国家自然科学基金资助项目 (U1936213)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0265
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 1
Section: Technology of Information Security
Pages: 272-277
Serial Number: 1001-3695(2023)01-045-0272-06

Publish History

[2022-08-15] Accepted Paper
[2023-01-05] Printed Article

Cite This Article

林兆亮, 李晋国, 黄润渴. V2G网络中基于联邦学习和CNN-BiLSTM的DDoS攻击检测 [J]. 计算机应用研究, 2023, 40 (1): 272-277. (Lin Zhaoliang, Li Jinguo, Huang Runke. DDoS in V2G: intrusion detection based on federated learning and CNN-BiLSTM [J]. Application Research of Computers, 2023, 40 (1): 272-277. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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