The leakage of source code will lead to a serious information security crisis. It is of great significance for electric power mobile micro-applications to detect key codes and then protect them. Aiming at the poor representation ability of JavaScript code, we suggested a key code detection model based on a semantic convolutional memory fusion network. The fusion network was constructed by combining TextCNN and Attention-based BiLSTM to extract code features. The lexical unit sequence that is abstracted from the abstract syntax tree will be input to the fusion network for identification and classification. According to the experimental results, the proposed model shows improvement in the representation ability and overall performance in key code detection.
KEYWORDS: Data modeling, Data fusion, Machine learning, Education and training, Feature fusion, Data conversion, Data communications, Data analysis, Data acquisition
The rapid development of edge network devices has led to the explosive growth of their data, and the difficulty of dealing with heterogeneous data in edge devices has been further increased. To solve the problem of heterogeneous data fusion without interaction, this paper proposes a data heterogeneous model analysis based on federated learning. Preprocess the multi-source heterogeneous data to obtain the main features of the condensed data. Then, the multi-source heterogeneous data nodes are positioned to avoid multi-fusion results, and Spatio-temporal correlation degree of the multi-source heterogeneous data is calculated to improve the accuracy of fusion. Finally, a multi-source heterogeneous data fusion model is established based on federated learning to ensure the security of data fusion. Compared with the traditional model, the data fusion of the proposed model is more stable, and the error is smaller. The effectiveness of the proposed model is verified by the stability and accuracy of the fusion of the heterogeneous data. The multi-source heterogeneous data fusion model studied in this paper can improve the quality of Internet of Things data and promote the development of edge devices in China.
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