Lightweight network intrusion detection method based on improved federated learning

Yang He; Yongliang Jia; Peng Tao

doi:10.1117/12.2641086

2 December 2022 Lightweight network intrusion detection method based on improved federated learning

Yang He, Yongliang Jia, Peng Tao

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881M (2022) https://doi.org/10.1117/12.2641086
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

Computer network has been widely used in all walks of life, network security has also received unprecedented attention, network intrusion detection technology is one of the key technologies to maintain network security. Traditional intrusion detection methods based on rules have some disadvantages, such as dependence on manual intervention, difficulty in updating rules database, and difficulty in detecting unknown intrusion. Therefore, a lightweight network intrusion detection method is designed based on improved federated learning. Firstly a network intrusion detection model is constructed based on improved federated learning to realize lightweight network intrusion detection. The experimental results show that the designed network intrusion detection method has good detection effect and has certain application value, which can be used as a reference for subsequent network intrusion detection.

Citation Download Citation

Yang He, Yongliang Jia, and Peng Tao "Lightweight network intrusion detection method based on improved federated learning", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881M (2 December 2022); https://doi.org/10.1117/12.2641086

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