I feel like you could make a comedy sketch from this: User downloads large log data history to make ML model => User trains new model from accessed data => User goes to upgrade algorithm by loading more data into model => Model notices user accessing more data than normal => User gets fired due to heightened security risk => Model never upgraded again.
So, create decoy anomalies and get all that has to be done from entities which can function around those anomalies, so the attention will never be drawn to them. And everything appears to fit within the square box.
Although you are correct from a Business Intelligence point of view, technically speaking there is still an initial core set of rules that defines the routine for the clustering of pathways - patterning and especially depatterning (spotting anomalies) -, soo the presenter is not entirely wrong (maybe he should've define what he meant by ''rules'').
I come from an ABA background, and a career in IT. This is the most fascinating thing I’ve heard in a long time. I want more content of this.
Thanks for the great feedback, Untap101! Here’s a playlist with all my videos: th-cam.com/play/PLdw2bVqUvsk2PnreCdr5uDZ1CjwvQScKe.html
I'm also in the ABA world looking to get into IT.
This presentation was explained extremely well. Thank you!!!
I feel like you could make a comedy sketch from this:
User downloads large log data history to make ML model => User trains new model from accessed data => User goes to upgrade algorithm by loading more data into model => Model notices user accessing more data than normal => User gets fired due to heightened security risk => Model never upgraded again.
Great video, great content !
So, create decoy anomalies and get all that has to be done from entities which can function around those anomalies, so the attention will never be drawn to them. And everything appears to fit within the square box.
User behavior analytics is not only about looking for anomalies.
Insightful
YOU GOT ME!
thanks! helped
Rollin Fork
Howell Park
Keira Shores
Helena Dam
I think you shouldn't mention "rules" and "machine learning" together. then its not really machine learning, its the traditional rule based system.
Although you are correct from a Business Intelligence point of view, technically speaking there is still an initial core set of rules that defines the routine for the clustering of pathways - patterning and especially depatterning (spotting anomalies) -, soo the presenter is not entirely wrong (maybe he should've define what he meant by ''rules'').
Ole Glens
Bryon Ridge
Blair Plain
Donnelly Grove
Dominic Walks
Roderick Shore
Crist Shores
Sigrid Lodge
Myrtie Road
Norwood Court
Bergstrom Street
Charlene Mission
Tamara Plain
Jaquan Gateway
Emile Route
Taylor Light
Federico Extension
Charlotte Key
Cronin Freeway
Violet Shoal
Frederik Trail
Hailee Bridge
Vivianne Groves
Lubowitz View
Abagail Underpass
O'Keefe Causeway
Glen Mountain
Mylene Wall
Johnston Rue
Boehm Freeway
Cartwright Springs
Bettye Mill
Sigrid Springs
Katlyn Roads
Jennings Lake
Richard Lights
Cortez Overpass
Gwendolyn Trail
Katlyn Lake
Green Glens
Rutherford Route
Koelpin Lane
Clifford Fork
Kessler Well
Padberg Turnpike
Erick Estate
Drew Track
Abigail Meadows
Corwin Green
Yvette Wells
Dan Well
Curtis Canyon
O'Keefe Drives
O'Reilly Station
Boyle Divide
Wilfrid Meadow
Bertha Coves
Swift Union
Brakus Greens
Trey Lights
Darby Springs
Rose Course
Maggie Brook
Schmeler Plain
Carley Highway
Frederique Route
Will Landing
Nader Prairie
Gerald Spur
Schimmel Wall
Vincenzo Cape
Grady Trace
Blanda Canyon
Billie Knoll
Moore Roads
Walton Parks
Rice Street
Tromp Squares
Waters Estates
Harber Ford
Padberg Rapid
Gia Camp
Kiehn Neck
Wintheiser Bridge
Ayana Roads
Jackson Drive