Research on PCA-DNN intrusion detection based on improved immune cloning algorithm

Zumin Wang; Qiang Li

doi:10.1117/12.2659603

22 November 2022 Research on PCA-DNN intrusion detection based on improved immune cloning algorithm

Zumin Wang, Qiang Li

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124751U (2022) https://doi.org/10.1117/12.2659603
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

In order to improve the efficiency of model learning and the accuracy and precision of detection results in network intrusion detection, an algorithm based on improved immune cloning algorithm to optimize neural network structure and parameters is proposed. The raw data is dimensionally reduced using principal component analysis and used as model input data. The neural network structure and parameters are used as immune antibodies. When the antibody group evolves slowly in the process of immune cloning, the antibodies with high affinity are used as booster vaccines to inoculate to achieve the optimal selection of neural network structure and parameters. The experimental results show that compared with the unoptimized detection model, the detection model optimized by traditional immune clones and the detection model improved by other optimization algorithms, the method improves the accuracy, precision and false alarm rate.

Citation Download Citation

Zumin Wang and Qiang Li "Research on PCA-DNN intrusion detection based on improved immune cloning algorithm", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124751U (22 November 2022); https://doi.org/10.1117/12.2659603

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