A Classification Approach Based on Divergence for Network Traffic in Presence of Concept Drift
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摘要: 网络流量特征分布的动态变化产生概念漂移问题，造成基于机器学习的网络流量分类模型精度下降.定期更新分类模型耗时且无法保证分类模型的泛化能力.基于此，提出一种基于散度的网络流概念漂移分类方法(ensemble classification based on divergence detection, ECDD)，采用双层窗口机制，从信息熵的角度出发，根据流量特征分布的JS散度，记为JSD(Jensen-Shannon divergence)来度量滑动窗口内数据分布的差异，从而检测概念漂移.借鉴增量集成学习的思想，检测到漂移时对于新样本重新训练出新的分类器，之后通过分类器权值排序，保留性能较高的分类器，加权集成分类结果对样本进行分类.抓取常见的网络应用流量，根据应用特征分布的不同构建概念漂移数据集，将该方法与常见的概念漂移检测方法进行实验对比，实验结果表明：该方法可以有效地检测概念漂移和更新分类器，表现出较好的分类性能.Abstract: Due to the high dynamic variability, suddenness and irreversibility of network traffic, the statistical characteristics and distribution of traffic may change dynamically, resulting in a concept drift problem based on the flow-based machine learning method. The problem of concept drift makes the classification model based on the original data set worse on the new sample, which causes the classification accuracy to decrease. Based on this, a classification approach based on divergence for network traffic in presence of concept drift, named ECDD (ensemble classification based on divergence detection) is proposed. The method uses a double-layer window mechanism to track the concept drift. From the perspective of information entropy, the Jensen-Shannon divergence is used to measure the difference of data distribution between old and new windows, so as to effectively detect the concept drift. This paper draws on the idea of incremental ensemble learning, trains a new classifier on the concept drift traffic based on the pre-retention classifier, and replaces the classifier with the original performance degradation according to the classifier weight, so that the ensemble classifier is effectively updated. For common network application traffic, this paper constructs a concept drift data set according to different application feature distributions. This paper compares the method with common concept drift detection methods and the experimental results show that the method can effectively detect concept drift and update the classifier, showing better classification performance.
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