<p>From what I read, I'd be surprised if they're using neural networks. Here's how they say they detect anomalies:</p>

<blockquote>
  <p>Detect outliers in a population by building a profile of a “typical” user or machine to know when one starts to stray from the pack.</p>
</blockquote>

<p>Doing anomaly detection like that requires nothing more than a statistical test of whether or not observed behavior is within 2-3 standard deviations of the expected behavior.</p>

<p>If you want to use neural networks for some reason, you could go with CNNs, RNNs, or attention-only networks. <a href="https://arxiv.org/abs/1706.03762" rel="nofollow noreferrer">Google recently showed that you don't need RNNs or CNNs to do state-of-the-art translation</a>.</p>
Creating anomaly detection using machine learning
<p>I'm very impressed from the new <a href="https://www.elastic.co/products/x-pack/machine-learning" rel="nofollow noreferrer">x-pack</a> ML of the elastic stack. It seems their technique learns data patterns over time and can predict anomalies in multiple domains.</p>

<p><a href="https://i.stack.imgur.com/TrGx5.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/TrGx5.png" alt="enter image description here"></a></p>

<p>Zoomed in:
<a href="https://i.stack.imgur.com/CfBdJ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/CfBdJ.png" alt="enter image description here"></a></p>

<p>I was wondering what approach and network topology could be used, in order to create a similar feature. Is it fair to assume that since x-pack works on time series data, RNN would be a good start?</p>

<p>Interested in your opinions and references.</p>