针对网络流量分类中少数恶意类难识别问题，提出分组重加权新方法。
Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification
- 将类别分组后迭代调整权重，降低类别不平衡影响
- 在多个基准上显著提升少数类识别率与整体性能
- 适合安全敏感的网络流量分析场景，尤其处理大量恶意类
互联网服务催生了海量网络流量，机器学习已成为关键工具，尤其在风险敏感的应用中。本文聚焦严重类别不平衡下的网络流量分类问题，此类分布会扭曲最优决策边界，导致性能不佳。现有方法难以应对大量少数恶意类别，带来安全隐患。为此，本文提出一种分组与重加权策略：受分组分布鲁棒优化启发，将类别启发式聚类为组，迭代更新各分类别的非参数权重，并通过最小化重加权损失来优化模型。我们从斯塔克尔伯格博弈角度理论解释优化过程，并在典型基准上进行广泛实验。结果表明，该方法不仅能缓解类别不平衡的负面影响，还能提升预测综合性能。
原文摘要 · Abstract (English)
Internet services have led to the eruption of network traffic, and machine learning on these Internet data has become an indispensable tool, especially when the application is risk-sensitive. This paper focuses on network traffic classification in the presence of severe class imbalance. Such a distributional trait mostly drifts the optimal decision boundary and results in an unsatisfactory solution. This raises safety concerns in the network traffic field when previous class imbalance methods hardly deal with numerous minority malicious classes. To alleviate these effects, we design a group & reweight strategy for alleviating class imbalance. Inspired by the group distributionally optimization framework, our approach heuristically clusters classes into groups, iteratively updates the non-parametric weights for separate classes, and optimizes the learning model by minimizing reweighted losses. We theoretically interpret the optimization process from a Stackelberg game and perform extensive experiments on typical benchmarks. Results show that our approach can not only suppress the negative effect of class imbalance but also improve the comprehensive performance in prediction.
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