Abnormal Network User Detection Method based on the Hybrid Neural Network from the Perspective of Multimodal Data Fusion

With the rapid development of the Internet, network user behavior data shows explosive growth. How to accurately identify abnormal users from massive data is of great significance for maintaining network security and preventing network crimes. This study first comprehensively collected multimodal data from users, including text data, behavioral data and possible image data. These data form the foundation of the research and provide rich materials for subsequent feature extraction and model construction. In the feature extraction stage, we adopted different processing methods for data of different modalities. For text data, natural language processing techniques are used to extract features such as keywords and emotional tendencies. Mining patterns and anomalies in user behavior through statistical analysis, time series analysis and other methods for behavioral data. By using computer vision technology to extract image features from image data, these features collectively constitute a multimodal feature set of user behavior. The experimental results show that the anomaly network user detection method based on multimodal data fusion and hybrid neural networks (HNN) has high accuracy and robustness. Compared with single modal data or traditional detection methods, this method shows significant advantages in identifying abnormal users. In addition, this method can provide rich user behavior characteristic information, which provides strong support for network security analysis.