Design of Financial Risk Intelligent Prediction and Network Security Threat Detection System Based on Self-Attention Mechanism

Abstract

Current financial risk prediction and network security threat detection tend to operate independently. The lack of cross-domain collaboration capabilities in existing methods when processing complex heterogeneous data often results in insufficient accuracy and difficulty coping with rapidly changing dynamic environments. This paper constructs an intelligent prediction and threat detection system based on the self-attention mechanism, which can provide more accurate and timely risk warning and protection through cross-domain data fusion. The system design mainly adopts a method of fusion for the financial data and network security data to co-model multimodal data and enhances the long-range dependency and relationship of data features by the SAM (Self-attention Mechanism); in the task of financial risk prediction, the paper combines LSTM with the SAM, further digging deeply into the data to excavate potential high-value correlation features that can enhance the forecasting ability of the company's future financial status. The system detects potential network attack behavior in real time combining CNN and the self-attention model in network security threat detection. Finally, the system uses cross-domain data fusion to enhance the accuracy and adaptability of risk prediction and threat detection, providing accurate and timely warning and protection in a dynamic risk environment. The experimental results show that the system combines LSTM with SAM in financial risk prediction, and the prediction accuracy is increased by 8% based on a single LSTM model, reaching 96.5%. Regarding network security threat detection, the system improved the detection accuracy and recall rate by 0.8% and 1.3% compared with the single CNN model, reaching 99.1% and 98%. In addition, the experiment also shows that the SAM-based model has strong real-time detection capabilities, can maintain low-latency response under large-scale data, adapt to dynamically changing network threats, and the system can have higher prediction and detection capabilities after parameter adjustment.