I added preliminary KMZ (zipped KML) support to APOLLO. If any APOLLO module’s SQL query has “Location” in its Activity field, it will extract the location coordinates in the column “Coordinates” as long as they are in Latitude, Longitude format (ie: 38, -77). These are more a less an upgrade/replacement from my previous iOS location scripts. (FYI: Those will not likely be updated further.)
You can find more details on the different modules and outputs here. The APOLLO output will also show counts of how many location data points were extracted from each module. An example from my own data contained 41,262 points! Due to the amount of data points and how applications like Google Earth might handle them I’ve decided to split them by module to be loaded separately. It’s not ideal, but my goal is not to crash Google Earth. (FWIW, I’m still working on other solutions, if you have experience in this area – drop me a line.) The most troublesome module (by a large magnitude) is routined_cache_zrtcllocationmo since it keeps track of extremely granular locations for about the last week. Mine had ~38,000 coordinates!
Let’s take a look at some examples! This data was collected on 07/18/2019 on iOS 12.1.1 to give you an idea on timeframe.
This one is from is from the routined_cloud_visit_inbound_start module. The earliest coordinate is from a few months prior from my trip to Amsterdam to teach FOR518 – Mac and iOS Forensic Analysis and Incident Response.
These are some of my “Significant Locations”, you can click on any coordinate and gather more detail. This is the same output that is captured in the APOLLO database or CSV file.
The next example is from routined_local_learned_location_of_interest_entry. Here you can see some of my travels since 2017! These contain the “Learned Locations of Interest”. You will probably see a bit more historical location data. Looks like I need to visit the middle of the US more!
Last but not least is the massive amount of data points from the routined_cache_zrtcllocatiomo module. This will show nearly exact, granular location for about the last week before collection. Here is my trip to Portland, OR for DFRWS! Not many guesses as to how I got to downtown Portland from the airport is there! (This is the module that produces many, many datapoints. It took patience with Google Earth to even get this screenshot, this is your warning.)
I hope this adds a bit of visual context to some of the APOLLO output, let me know if you have different ideas on how to represent some of this data! Pictures can certainly tell a story where it might otherwise get lost in the noise.
*** This is a Security Bloggers Network syndicated blog from SANS Digital Forensics and Incident Response Blog authored by sansdfir. Read the original post at: http://feedproxy.google.com/~r/SANSForensics/~3/-ic3FiiHmv8/ios-location-mapping-with-apollo-part-1