基于小样本学习的网络异常流量检测

Abnormal Network Traffic Detection Based on Small Sample Learning

网络结构具有较高复杂性,因此导致各种异常流量现象层出不穷,其中包括一些标注样本极少的新型异常流量类型。为了有效识别标注样本量极少的异常情况,设计了一种基于小样本学习的网络异常流量检测方法。该方法利用基于小样本的迁移学习技术识别异常流量,从而确保了网络安全。

The high complexity of network structure leads to various abnormal traffic phenomena,including some new abnormal traffic types with few labeled samples.In order to effectively identify the abnormal situations with few labeled samples,an abnormal network traffic detection method based on small sample learning is designed.The method uses the transfer learning technology based on small samples to identify the abnormal traffic.Thus,it can ensure the network security.