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Articles by Camilla Montonen

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Combining supervised and unsupervised machine learning for DGA detection

In this blog, we announce our first-ever supervised ML and security integration. This offers users a supervised ML solution package to detect domain generation algorithm (DGA) activity in your network data.

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Train, evaluate, monitor, infer: End-to-end machine learning in Elastic

To use machine learning in the Elastic Stack, all you need is for your data to be stored in Elasticsearch. Learn how to extract valuable insights from your data with a few clicks and build a fully operational end-to-end machine learning pipeline.

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Machine learning in cybersecurity: Detecting DGA activity in network data

Bad actors (and their malware) use domain generated algorithms (DGA) to avoid detection, but with Elastic machine learning, you can easily build models to help you see right through their tricks. Learn how in part 2 of this series.

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Machine learning in cybersecurity: Training supervised models to detect DGA activity

Bad actors (and their malware) use domain generated algorithms (DGA) to avoid detection, but with Elastic machine learning, you can easily build models to help you see right through their tricks.

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Interpretability in ML: Identifying anomalies, influencers, and root causes

How does the Elastic Machine Learning product compute influencers, and what do influencers say about the root cause of an anomaly?

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Catching malware with Elastic outlier detection

Signature-based anti-malware techniques struggle to keep up with new malware variants. Can outlier detection offer new ways to help detect malicious binaries?

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Interpreting multi-bucket impact anomalies using Elastic machine learning features

What are multi-bucket impact anomalies? How should one interpret them? What are some gotchas to keep in mind? Read on for answers to these questions and more.