While encryption ensures the confidentiality and integrity of user data, more and more attackers try to hide attack behaviours through encryption, which brings new challenges to malicious traffic identification. How to effectively detect encrypted malicious traffic without decrypting traffic and protecting user privacy has become an urgent problem to be solved. Most of the current research only uses a single CNN, RNN, and SAE network to detect encrypted malicious traffic, which does not consider the forward and backward correlation between data packets, so it is difficult to effectively identify malicious features in encrypted traffic. This study proposes an approach that combines spatial-temporal feature with dual-attention mechanism, which is called TLARNN. Specifically, first we use 1D-CNN and BiGRU to extract spatial features in encrypted traffic packets and temporal features between encrypted streams, respectively, which enriches the features of different dimensions, and then, the soft attention mechanism is focused on the encrypted data packets to extract features. Ultimately, the second layer of the soft attention mechanism is used for aggregating malicious features. Several comparative experiments are designed to prove the effectiveness of the proposed scheme. The experimental results demonstrate that the proposed scheme has a significant performance improvement compared to existing ones.

To ensure the safety of the massive growth of distributed photovoltaic grid-connected inverters and the security of backhaul data in the context of new power systems, research on anomaly detection has also been put on the agenda. The data of the photovoltaic grid-connected inverter has complex time dependence and uncertainty, and the data security problem is prone to occur in the process of data transmission, and the existing anomaly detection means do not comprehensively consider these factors, and cannot timely find the abnormal phenomena caused by the synthesis of data security and their faults, which leads to the tampering of power data and the increase of operation and maintenance costs. To solve the above problems, an anomaly detection method integrating a long short-term memory network (LSTM) and serial depth autoencoder (DAE) is proposed based on edge computing, characterized by the power and voltage of the device, the length, and the delay of the data. Firstly, the method performed time series transformation processing on the original training dataset through the sliding window. Then, the model was trained using LSTM and DAE. Finally, the decoding error of the test data was compared with the threshold obtained by the training. The example verification shows that the model has achieved good performance in the recall rate, accuracy rate and F1 score, and can effectively detect the equipment that produces its fault or data return abnormality in the photovoltaic inverter. So, the model has good industrial practical value.

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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