Summary of Early-stage Anomaly Detection: a Study Of Model Performance on Complete Vs. Partial Flows, by Adrian Pekar and Richard Jozsa
Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial Flows
by Adrian Pekar, Richard Jozsa
First submitted to arxiv on: 3 Jul 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 The paper investigates the effectiveness of machine learning models in detecting anomalies in computer networks by analyzing how they perform when trained and tested on complete or incomplete data. The study finds that models trained on complete data can struggle with partial data, leading to a significant drop in precision and recall (up to 30%). However, models trained and tested on consistent complete or partial datasets remain robust. The research also reveals the importance of having at least 7 packets in the test set for reliable detection rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how machine learning models can help detect unusual activity in computer networks. It compares what happens when these models are trained and tested using either all data or only part of it. The results show that models that learn from complete data might not work as well with incomplete data, which could be a problem for real-time detection. However, models that are trained and tested on consistent amounts of data stay reliable. This study helps us understand how to use machine learning in network security. |
Keywords
» Artificial intelligence » Machine learning » Precision » Recall
