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

arXiv: https://arxiv.org/abs/2410.18380 · HTML: https://mlsanity.com/paper/2410.18380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Meta-learning for Positive-unlabeled Classification](https://mlsanity.com/paper/2406.03680/) (2024)

## Built on by (most cited first, 1 of 1)

- [PUL-Inter-slice Defender: An Anomaly Detection Solution for Distributed Slice Mobility Attacks](https://mlsanity.com/paper/2510.02236/) (2025)

## Abstract (arXiv)

This paper explores the application of Positive-Unlabeled (PU) learning for enhanced Distributed Denial-of-Service (DDoS) detection in cloud environments. Utilizing the textttBCCC-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 F1 scores exceeding 98%. We quantify the efficacy of each approach using metrics including F1 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.