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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Jay Stokes</author_name><author_url>https://www.microsoft.com/en-us/research/people/jstokes/</author_url><title>Unsupervised Streaming Cyber-Analytics - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="BsUacQ2wV8"&gt;&lt;a href="https://www.microsoft.com/en-us/research/video/unsupervised-streaming-cyber-analytics/"&gt;Unsupervised Streaming Cyber-Analytics&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/video/unsupervised-streaming-cyber-analytics/embed/#?secret=BsUacQ2wV8" width="600" height="338" title="&#x201C;Unsupervised Streaming Cyber-Analytics&#x201D; &#x2014; Microsoft Research" data-secret="BsUacQ2wV8" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><thumbnail_url>https://www.microsoft.com/en-us/research/wp-content/uploads/2018/03/40725.jpg</thumbnail_url><thumbnail_width>1281</thumbnail_width><thumbnail_height>717</thumbnail_height><description>The supervised learning approach to cyber-analytics has proven rather successful. However, there are challenges with this approach, including a frequent dearth of labelled data, the issue of temporal variation, and fundamentally, problems of data volume and velocity. In this talk, we describe simple unsupervised analytics intended to complement and enhance supervised methods. These approaches are [&hellip;]</description></oembed>
