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From "Edwards, Brandon" <>
Subject Re: Spot Suspicious Connects Description and questions related to 'feedback' from UI to ML
Date Thu, 25 May 2017 18:11:43 GMT
Ok this should work:

Can someone confirm? THX

On 5/25/17, 11:08 AM, "Segerlind, Nathan L" <> wrote:

    The link does not work for me.... I keep getting a message that reads "folder Spot does
not exist"
    -----Original Message-----
    From: Edwards, Brandon [] 
    Sent: Thursday, May 25, 2017 10:57 AM
    Subject: Re: Spot Suspicious Connects Description and questions related to 'feedback'
from UI to ML
    Oh I had the scoring values reversed. Thanks Alan!
    Also here is a link to the file on Dropbox:
    On 5/25/17, 10:50 AM, "Alan Ross" <> wrote:
        On the scoring piece.  1 has traditionally been "Bad" and 3 has been
        "Benign".  Are we changing that?
        On Thu, May 25, 2017 at 10:49 AM, Alan Ross <> wrote:
        > I don't believe this list permits attachments Brandon.  Perhaps post it to
        > google docs and send out a link?
        > Alan
        > On Thu, May 25, 2017 at 10:27 AM, Edwards, Brandon <
        >> 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.
        >> 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
        >> 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
        >> 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
        >> 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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