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

Abstract

This paper explores the application of Positive-Unlabeled (PU) learning for enhanced Distributed Denial-of-Service (DDoS) detection in cloud environments. Utilizing the BCCC-cPacket-Cloud-DDoS-2024 dataset, we implement PU learning with four machine learning algorithms: XGBoost, Random Forest, Support Vector Machine, and Na\"ive 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.

Tasks

Reproductions