Optimization and Performance Evaluation of Network Intrusion Detection and Prevention Technology Based on Attention Mechanism

With the increasing complexity of cyber-attacks, the traditional network intrusion detection and defense technologies face great challenges. In order to improve network security, this paper proposes an optimization scheme of network intrusion detection and defense technology based on attention mechanism. To solve these problems, this paper adopted the attention mechanism in deep learning, aiming to improve the ability of the model to identify key features, thus enhancing the detection accuracy. This paper designs a new network architecture that combines convolutional neural network (CNN) and attention mechanism to extract important information in network traffic data. The performance of the proposed method was evaluated by comparative experiments on multiple publicly available datasets. The experimental results of this method on a dataset show that the network intrusion detection method based on attention mechanism can achieve a maximum accuracy of 96% and an precision of 97.9%. The network intrusion detection and defense technology based on attention mechanism not only improves the detection performance, but also effectively reduces the false positives. This study provides a new idea for network security protection, emphasizes the importance of intelligent technology in the field of network security, and looks forward to further verify its effectiveness and feasibility in practical application.