用少量标注数据提升云环境下的DDoS检测精度
Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis
- 基于正例-未标记学习，仅用部分标注数据训练模型
- XGBoost和随机森林F1超过98%，表现最佳
- 适合标签稀缺的云安全场景，可推广至多云环境
本文研究了正例-未标记（PU）学习在云环境中增强分布式拒绝服务（DDoS）检测的应用。利用《BCCC-cPacket-Cloud-DDoS-2024》数据集，我们采用XGBoost、随机森林、支持向量机和朴素贝叶斯四种机器学习算法实现PU学习。实验结果表明，集成方法表现更优，其中XGBoost与随机森林的F₁分数均超过98%。通过F₁分数、ROC AUC、召回率和精确率等指标量化各方法效能。本研究弥合了PU学习与云环境异常检测之间的差距，为多云环境下上下文感知的DDoS检测提供了基础。研究结果凸显了在标注数据有限情况下，PU学习在构建更鲁棒、自适应的云安全机制中的潜力。
原文摘要 · Abstract (English)
This paper explores the application of Positive-Unlabeled (PU) learning for enhanced Distributed Denial-of-Service (DDoS) detection in cloud environments. Utilizing the $\texttt{BCCC-cPacket-Cloud-DDoS-2024}$ dataset, we implement PU learning with four machine learning algorithms: XGBoost, Random Forest, Support Vector Machine, and Naïve Bayes. Our results demonstrate the superior performance of ensemble methods, with XGBoost and Random Forest achieving $F_{1}$ scores exceeding 98%. We quantify the efficacy of each approach using metrics including $F_{1}$ score, ROC AUC, Recall, and Precision. This study bridges the gap between PU learning and cloud-based anomaly detection, providing a foundation for addressing Context-Aware DDoS Detection in multi-cloud environments. Our findings highlight the potential of PU learning in scenarios with limited labeled data, offering valuable insights for developing more robust and adaptive cloud security mechanisms.
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