Research Article
Machine Learning Based Method for Mining Anomaly Features of Network Multi Source Data
@INPROCEEDINGS{10.1007/978-3-031-50577-5_17, author={Lei Ma and Jianxing Yang and Jingyu Li}, title={Machine Learning Based Method for Mining Anomaly Features of Network Multi Source Data}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III}, proceedings_a={ICMTEL PART 3}, year={2024}, month={2}, keywords={Machine Learning Network Multi-source Data Abnormal Data Data Mining}, doi={10.1007/978-3-031-50577-5_17} }- Lei Ma
Jianxing Yang
Jingyu Li
Year: 2024
Machine Learning Based Method for Mining Anomaly Features of Network Multi Source Data
ICMTEL PART 3
Springer
DOI: 10.1007/978-3-031-50577-5_17
Abstract
Aiming at the problem that the current network multi-source data anomaly diagnosis is not effective, this paper proposes a method of network multi-source data anomaly feature mining based on machine learning. First of all, a multi-source data feature recognition model is built based on the multi-level structure of machine learning. Then, the network multi-source data feature classification algorithm is designed and optimized to identify and locate the abnormal data features based on the classification results. Finally, the network multi-source data abnormal data screening model is constructed to mine the abnormal characteristics. The experimental results show that this method has high practicability and accuracy, and fully meets the research requirements.
