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From Raymundo Panduro <raypand...@gmail.com>
Subject Re: Spot Suspicious Connects Description and questions related to 'feedback' from UI to ML
Date Thu, 25 May 2017 17:46:15 GMT
@Brandon I think you need to upload the file to a google drive or dropbox
and send the link because the attachment It was removed.





*Raymundo Panduro*

On Thu, May 25, 2017 at 12:27 PM, Edwards, Brandon <
brandon.edwards@intel.com> wrote:

> Hi all,
>
>
>
> I am attaching the document that describes how Spot uses LDA in order to
> perform anomaly detection on network events. I have also received multiple
> questions related to how the ‘user scoring’ (‘feedback’) of particular
> items in the suspicious connects report (in the UI layer) is used in ML. We
> have not provided much detail on this functionality in the attached
> document. I thought I’d put an explanation out there and we can discuss
> questions related to my explanation and discuss what additional info should
> be included in the attached document.
>
>
>
> The Spot team feels that changes are needed to this ‘feedback’
> functionality, and see these changes as happening concurrent with
> improvements to the ability for context from an LDA model trained on a
> given batch of data to be carried forward to the next training run (or even
> training in a streaming use case). The value of ‘feedback’ is dependent on
> the quality of the model-context we can carry over.
>
>
>
> The idea for feedback is as follows. The items that are scored with a 1
> (i.e. the user identifies the item as benign and so does not want to see it
> in the suspicious connects report anymore) will be used for letting the
> machine learning component know that such an entry should not be considered
> as suspicious anymore. Currently this is done by injecting artificial log
> entries into the next batch of data so that LDA sees many such entries and
> therefore no longer sees them as anomalies.
>
>
>
> We have ideas for other ways to allow this functionality - for example we
> could filter entries matching the identified pattern from the next batch
> run BEFORE ML runs on the batch. For items that are scored by the user in
> the UI as ‘3’ (for example the user sees an ip as so suspicious that we
> want to see all future log entries associated to that ip) we could filter
> future items matching such a pattern in order to skip ML and instead report
> them in a separate pane of the UI or insert them to the top of the most
> suspicious events.
>
>
>
> Comments, Questions?
>
> Brandon
>

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