Research on Computer Network Intrusion Detection Algorithm Based on Deep Learning

In this paper, a novel Deep Forest Q-Random Network (BFQ-GN) algorithm is proposed, which is designed to be applied to computer network intrusion detection based on deep learning. The algorithm combines the advantages of Random Forest (RF) and Deep Q-Network (DQN), and uses Transformer model for feature extraction. BFQ-GN algorithm can deal with high-dimensional data effectively and provide stable classification performance by combining RF decision trees. DQN continuously optimizes its strategy by interacting with the environment, and can adaptively adjust the detection strategy in the dynamic network environment to improve the accuracy. Transformer, on the other hand, uses a self-attention mechanism to capture the importance of long dependencies in network traffic and the dynamic adjustment of complex patterns, thereby improving the accuracy and adaptability of feature extraction. In the experimental part, we compare the BFQ-GN with the traditional detection model. The results show that BFQ-GN can automatically learn features in data without human intervention, and can monitor network traffic in real time to detect potential intrusion behaviors in time. This fully verifies the feasibility and effectiveness of this architecture in computer network intrusion detection algorithms based on deep learning.