【摘要】 在当今互联网飞速发展的时代,人们在网络中信息交互的次数日益增多,使得网络安全显得尤为重要。文章以增强模型检测异常流量的能力为研究目的,提出一种基于注意力机制的胶囊网络模型。在特征提取阶段和动态路由阶段分别融入注意力机制,增强了模型提取关键特征的能力,提升了在入侵检测任务中的准确率。在NSL-KDD数据集和CICDS2017数据集进行实验,结果表明文章所提模型在泛化能力方面高于其他模型,在CICIDS2017的测试集上,准确率达97.56%;在NSL-KDD的测试集上,准确率可达95.88%。相较于其他传统常用的入侵检测模型,效率有显著提升。更多还原

【Abstract】 In the era of rapid development of the Internet, the number of people interacting with each other on the Internet is increasing, making network security particularly important. This paper aimed to enhance the model’s ability to detect abnormal traffic,and proposed a capsule network model based on the attention mechanism. In the feature extraction stage and the dynamic routing stage, the attention mechanism was incorporated to enhance the model’s ability to extract key features and improve the accuracy of intrusion detection tasks. Through experiments on the NSL-KDD data set and the CICDS2017 data set,experimental results show that the model in this paper is higher than other models in terms of generalization ability, and the accuracy rate on the CICIDS2017 test set has reached 97.56%.The accuracy of the NSL-KDD test set can reach 95.88%, which is significantly more efficient than other traditional intrusion detection models.更多还原