Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis

TL;DR

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.

Abstract

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.

Figures (11)

• Figure 1: PU Learning Trial 1
• Figure 2: PU Learning Trial 2
• Figure 3: PU Learning Trial 3
• Figure 4: PU Learning Trial 4
• Figure 5: Comparison of Machine Learning (ML) Model Performance Metrics for DDoS Detection using PU Learning