| Title | Improved Depth Neural Network Industrial Control Security Algorithm Based On PCA Dimension Reduction |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Qiang, Rong |
| Conference Name | 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) |
| Date Published | mar |
| Keywords | adagrad, Deep Learning, Deep Neural Network, dimensionality reduction, feature extraction, industrial control, industrial control system, industrial control systems, machine learning algorithms, Neural networks, PCA, pubcrawl, Resiliency, Scalability, scalable systems, Software algorithms |
| Abstract | In order to improve the security and anti-interference ability of industrial control system, this paper proposes an improved industrial neural network defense method based on the PCA dimension reduction and the improved deep neural network. Firstly, the proposed method reduces the dimensionality of the industrial data using the dimension reduction theory of principal component analysis (PCA). Then the deep neural network extracts the features of the network. Finally, the softmax classifier classifies industrial data. Experiment results show that compared with unintegrated algorithm, this method achieves higher recognition accuracy and has great application potential. |
| DOI | 10.1109/AEMCSE51986.2021.00181 |
| Citation Key | qiang_improved_2021 |
Improved Depth Neural Network Industrial Control Security Algorithm Based On PCA Dimension Reduction