Summary of Harnessing Pu Learning For Enhanced Cloud-based Ddos Detection: a Comparative Analysis, by Robert Dilworth and Charan Gudla
Harnessing PU Learning for Enhanced Cloud-based DDoS Detection: A Comparative Analysis
by Robert Dilworth, Charan Gudla
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the application of Positive-Unlabeled (PU) learning to enhance Distributed Denial-of-Service (DDoS) detection in cloud environments. Using the dataset, four machine learning algorithms – XGBoost, Random Forest, Support Vector Machine, and Naïve Bayes – are implemented with PU learning. The results show that ensemble methods excel, with XGBoost and Random Forest achieving F_{1} scores above 98%. Metrics such as F_{1} score, ROC AUC, Recall, and Precision quantify the effectiveness of each approach. 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. The 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to improve detecting bad traffic on computer networks (DDoS attacks) using a special type of machine learning called Positive-Unlabeled learning. The researchers used a dataset from 2024 to test four different machine learning algorithms: XGBoost, Random Forest, Support Vector Machine, and Naïve Bayes. They found that combining these algorithms worked really well, with two of them achieving scores over 98%. This research helps bridge the gap between this type of learning and detecting bad traffic in cloud computer systems. The results show how PU learning can be useful when there’s limited information to work with, which could help create better security systems for cloud computing. |
Keywords
» Artificial intelligence » Anomaly detection » Auc » Machine learning » Precision » Random forest » Recall » Support vector machine » Xgboost
