提出评估数据集稳定性的新方法，发现网络流量数据随时间变化会引发模型性能骤降。
Drift-Based Dataset Stability Benchmark
- 基于概念漂移检测与特征权重增强，量化数据集稳定性。
- 在CESNET-TLS-Year22数据集上识别出关键不稳定时段和特征退化点。
- 适合关注模型长期鲁棒性的系统部署与数据维护团队使用。
机器学习在流量分类中应用广泛，但网络协议快速演进导致训练数据过时，引发模型性能下降。当某类流量行为发生显著变化（即特征分布改变）时，模型可能出现突然失效，称为数据或概念漂移。现有做法多采用完全重训，但往往忽略数据质量本身问题。本文提出一种新型数据集稳定性评估方法及可复用的基准工作流，基于概念漂移检测并结合机器学习特征权重以提升检测灵敏度。在CESNET-TLS-Year22数据集上的实验证明该方法能有效识别数据稳定性问题，提供首个数据集稳定性基准，揭示优化方向，并验证了数据变体优化对稳定性的积极影响。
原文摘要 · Abstract (English)
Machine learning (ML) represents an efficient and popular approach for network traffic classification. However, network traffic classification is a challenging domain, and trained models may degrade soon after deployment due to the obsolete datasets and quick evolution of computer networks as new or updated protocols appear. Moreover, significant change in the behavior of a traffic type (and, therefore, the underlying features representing the traffic) can produce a large and sudden performance drop of the deployed model, known as a data or concept drift. In most cases, complete retraining is performed, often without further investigation of root causes, as good dataset quality is assumed. However, this is not always the case and further investigation must be performed. This paper proposes a novel methodology to evaluate the stability of datasets and a benchmark workflow that can be used to compare datasets. The proposed framework is based on a concept drift detection method that also uses ML feature weights to boost the detection performance. The benefits of this work are demonstrated on CESNET-TLS-Year22 dataset. We provide the initial dataset stability benchmark that is used to describe dataset stability and weak points to identify the next steps for optimization. Lastly, using the proposed benchmarking methodology, we show the optimization impact on the created dataset variants.
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