Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909382032228352 |
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| author | Dilworth, Robert Gudla, Charan |
| author_facet | Dilworth, Robert Gudla, Charan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18380 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis Dilworth, Robert Gudla, Charan Machine Learning Cryptography and Security 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. |
| title | Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2410.18380 |