good question - 99% of the time, we are installed on "client" controled equipment and networks. So, we are simply just another "application" on their servers. If someone is trying to crack-in to steal their data, they will do it via "normal" methods - so it is up to the client to have the proper securtiy controls in place so their envs are not compromised.
False positives -- often, it happens when you have someone like JOHN SMITH -- and that is the only data you have... no date-of-birth, no other discerning details..... so, it becomes and all-or-nothing type of a situation.
yes - that is very correct -- the pattern is the variation (e.g., trying not to be detected and varying their behavior). We also see this a lot with typos and spelling variations for names, ids, addresses...
I am more free to do what I need to do --- since I am often trying to "detect" the pattern. Once we have vetted the parameters/values - then they can be quantified a bit more into more automated procedures with the proper checking/controls.
Any results that come back with more than say 3 or 4 or 5 values would be indicative of someone trying to avoid detection -- why would you go to multiple sites to move money -- that is like going to multiple post-offices to mail letters....
A lot of the "proactive" targeting I do on transactional data is a simple set of aggregations --- so, for example, count all the transactions where multiple agents/sites were used in a certain time frame by the same person...
So, you might see a sequence of numbers - and one of the numbers just does not naturally fit -- that could be the basis for a new pattern.... ultimately, whatever is used, is to expose things we did no know previously.
I am thinking of mathematical prodigies who are able to eyeball quantities of raw data and recognize patterns. This was represented in the movie "A Brilliant Mind" - Am I completely off-base in thinking of visualization as an automated way of reproducing this rare skill?
yes - any data can be analyzed without visualization -- simply sort a column in a spreadsheet - -and then eyeball repetative values -- that is simple/easy to do --- but, not necessarily "scalable" for large volumes of data
And, think about it --- the LPR data can come from police cruisers... and in some places, tow truck drivers (banks pay for them so they can find deliquent car payments) -- and that might even be turned into a commerical data venture -- just like Google has their street-view....
For example - we are getting a lot of exposure on LPR data -- (license plate recognition). We don't make the units that capture the plates, but we can do analyis on the results - -and more importantly, fuse the data from multiple disparate sites... so the analyst/investigator can get a "better" picture of his target vehicle.
Yes - there are situations - that sometime cause concerns.... Sometimes, it reflects back on the agency - that we find things that should have been addressed, are wrong, or have been discarded/discounted...
Thanks to both of you. Chris, I am still having a hard time visualizing what you actually see when you mine diverse data - emails, posts, text, video - are you just looking at numbers, sources and so on?
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