Compare with PBCNN.

Network intrusion detection plays a crucial role in ensuring network security by distinguishing malicious attacks from normal network traffic. However, imbalanced data affects the performance of intrusion detection system. This paper utilizes few-shot learning to solve the data imbalance problem caused by insufficient samples in network intrusion detection, and proposes a few-shot intrusion detection method based on prototypical capsule network with the attention mechanism. Our method is mainly divided into two parts, a temporal-spatial feature fusion method using capsules for feature extraction and a prototypical network classification method with attention and vote mechanisms. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods on imbalanced datasets.

网络入侵检测（Network Intrusion Detection）通过区分恶意攻击与正常网络流量，在保障网络安全领域发挥着至关重要的作用。然而，数据不平衡问题会严重影响入侵检测系统的检测性能。本文针对网络入侵检测中样本不足引发的数据不平衡问题，采用少样本学习（Few-shot Learning）方法予以解决，并提出了一种基于带注意力机制的原型胶囊网络的少样本入侵检测方法。所提方法主要分为两部分：一是采用胶囊网络开展特征提取的时空特征融合方法，二是融合注意力与投票机制的原型网络分类方法。实验结果表明，所提出的模型在不平衡数据集上的性能优于现有最优方法。