Summary of Anomalyaid: Reliable Interpretation For Semi-supervised Network Anomaly Detection, by Yachao Yuan et al.
AnomalyAID: Reliable Interpretation for Semi-supervised Network Anomaly Detection
by Yachao Yuan, Yu Huang, Jin Wang
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed AnomalyAID framework addresses the challenges of semi-supervised learning in network anomaly detection by providing interpretable results and improving the reliability of interpretation outputs. The framework combines a novel interpretation approach with a two-stage semi-supervised learning framework to align model predictions with specific constraints. Experimental results demonstrate accurate detection results with reliable interpretations for semi-supervised network anomaly detection systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Semi-supervised learning helps detect anomalies in networks, but it’s hard when there’s limited labeled data. Also, it’s hard to understand why the algorithm is making certain decisions. This paper proposes a new framework called AnomalyAID that makes the process more understandable and reliable. It uses two stages of machine learning to help identify abnormal patterns in network data. |
Keywords
» Artificial intelligence » Anomaly detection » Machine learning » Semi supervised
