Explore the Intrusion Detection Model Based on Deep Reinforcement Learning from the Perspective of Data Analysis

In the digital age, the economy's growing dependence on network-based operations makes economic-field network traffic data highly complex in spatial and temporal aspects. Thus, data analysis is essential for both intrusion detection and protecting economic activities from cyber threats. This study explores the application efficiency and value of an innovative intrusion detection model based on deep reinforcement learning in the economic domain. From a data-analysis perspective, a bifurcated convolutional neural network extracts spatial features from economic-related network traffic data. Its structure preserves overall data characteristics and uncovers latent features of malicious intrusions. Then, a gated recurrent unit network analyzes time-series patterns to find temporal features, crucial for identifying normal or malicious behavior in economic activities. Using the KDDCUP99 dataset, the model is trained, verified, and tested. Parameter adjustments are guided by data analysis. Experimental results show that the model has a higher detection accuracy than traditional machine-learning models. This provides a more secure network environment for economic activities and brings potential economic benefits, making it a more effective and valuable network-security solution in the economic field.