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 Na\"{i}ve 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.

翻译：本文探讨了正例-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$\texttt{BCCC-cPacket-Cloud-DDoS-2024}$数据集，我们使用四种机器学习算法实现了PU学习：XGBoost、随机森林、支持向量机和朴素贝叶斯。我们的结果表明集成方法具有优越性能，其中XGBoost和随机森林的$F_{1}$分数超过98%。我们使用$F_{1}$分数、ROC AUC、召回率和精确率等指标量化了每种方法的效能。本研究弥合了PU学习与云端异常检测之间的差距，为处理多云环境中的情境感知DDoS检测奠定了基础。我们的发现凸显了PU学习在标记数据有限场景中的潜力，为开发更鲁棒、自适应的云安全机制提供了重要见解。