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<h1 class="title"><a id="machine-learning"></a>Anomaly Detection with Machine Learning<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/stack-docs/edit/7.5/docs/en/siem/machine-learning.asciidoc">edit</a><a class="xpack_tag" href="/subscriptions"></a></h1>
</div></div></div>
<p>For <span class="strong strong"><strong>Free Trial</strong></span>, <span class="strong strong"><strong><a href="/cloud/elasticsearch-service/signup?baymax=docs-body&elektra=docs" class="ulink" target="_top">Cloud</a></strong></span>
and <span class="strong strong"><strong><a href="/subscriptions" class="ulink" target="_top">Platinum License</a></strong></span> deployments,
<a href="/guide/en/kibana/7.5/xpack-ml.html" class="ulink" target="_top">Machine Learning</a> functionality is available
throughout the SIEM app. You can view the details of detected anomalies within
the <code class="literal">Anomalies</code> table widget shown on the Hosts, Network and associated Details
pages, or even narrow to the specific date range of an anomaly from the <code class="literal">Max
Anomaly Score</code> details in the overview of the Host and IP Details pages. Each
of these interfaces also offer the ability to drag and drop details of the
anomaly to Timeline, such as the <code class="literal">Entity</code> itself, or any of the associated
<code class="literal">Influencers</code>.</p>
<div class="imageblock screenshot">
<div class="content">
<img src="ml-ui.png" alt="ml ui">
</div>
</div>
<h3><a id="manage-jobs"></a>Manage machine learning jobs<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/stack-docs/edit/7.5/docs/en/siem/machine-learning.asciidoc">edit</a></h3>
<p>For users with the <code class="literal">ml_admin</code> role, the <code class="literal">Anomaly Detection</code> interface within
the main navigation header can be used for for viewing, starting, and stopping
SIEM machine learning jobs.</p>
<div class="tip admon">
<div class="icon"></div>
<div class="admon_content">
<p>To add a custom job to the <code class="literal">Anomaly Detection</code> interface, add a <code class="literal">SIEM</code> tag
to the job’s <code class="literal">Group</code> field (Kibana → Machine learning → Create/Edit job → Job
details).</p>
</div>
</div>
<h4><a id="included-jobs"></a>Prebuilt jobs<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/stack-docs/edit/7.5/docs/en/siem/machine-learning.asciidoc">edit</a></h4>
<p>The SIEM app comes with prebuilt machine learning anomaly detection jobs for automatically detecting
host and network anomalies. The jobs are displayed in the <code class="literal">Anomaly Detection</code>
interface. They are available when:</p>
<div class="ulist itemizedlist">
<ul class="itemizedlist">
<li class="listitem">
You ship data using <a href="/products/beats" class="ulink" target="_top">Beats</a>, and
Kibana is configured with the required index patterns
(<code class="literal">auditbeat-*</code>, <code class="literal">filebeat-*</code>, <code class="literal">packetbeat-*</code>, or <code class="literal">winlogbeat-*</code> via Kibana → Management → Index Patterns).
</li>
<li class="listitem">
Your shipped data is ECS-compliant, and Kibana is configured with the data’s
index patterns.
</li>
</ul>
</div>
<div class="note admon">
<div class="icon"></div>
<div class="admon_content">
<p>Machine learning jobs look back and analyse two weeks of historical data
prior to the time they are enabled. After jobs are enabled, they continuously
analyse incoming data. When jobs are stopped and restarted within the two week
timeframe, previously analysed data is not processed again.</p>
</div>
</div>
<p><a class="xref" href="prebuilt-ml-jobs.html" title="Prebuilt job reference">Prebuilt job reference</a> describes all available machine learning jobs and lists
which beats are required on your hosts for each job. For information on tuning
anomaly results to reduce the number of false positive, see
<a class="xref" href="tuning-anomaly-results.html" title="Optimizing anomaly results">Optimizing anomaly results</a>.</p>
<h3><a id="view-anomolies"></a>View detected anomalies<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/stack-docs/edit/7.5/docs/en/siem/machine-learning.asciidoc">edit</a></h3>
<p>To view the <code class="literal">Anomalies</code> table widget and <code class="literal">Max Anomaly Score By Job</code> details,
the user must have the <code class="literal">ml_admin</code> or <code class="literal">ml_user</code> role.</p>
<div class="note admon">
<div class="icon"></div>
<div class="admon_content">
<p>To adjust the <code class="literal">score</code> threshold that determines which
<a href="/guide/en/machine-learning/7.5/xpack-ml.html" class="ulink" target="_top">anomalies</a> are shown, you can modify Kibana →
Management → Advanced Settings → <code class="literal">siem:defaultAnomalyScore</code>.</p>
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