Skip to main content

Welcome to Mark Baggett - In Depth Defense

I am the course Author of SANS SEC573 Automating Information Security with Python. Check back frequently for updated tools and articles related to course material.




I know where you live... or at least google does

Can you use YouTube.com to find out where a video was uploaded? I’m not saying you can. I’m not saying you can’t. But I think it is interesting to try. Using the following method YouTube has led me to the homes of a few people I know. Does it work for you??

Start with YouTubes “Advanced Search”.

http://www.youtube.com/results?search_type=&search_query=#



Click “Advanced Options” and “Show Map”. Type in the userid of the person your trying to location and click the SEARCH box inside the advanced search box (not the one at the top). If the video is in the circle it will be displayed in the result. If not you will see “No Videos found for xyz” and a playlist for the user you are searching for. The difference between a hit/no hit is subtle. Do a search for something you know is geoencoded so you can see the differenece. As a rule, if you see this then the video is not in the circle.

No videos found for “USERXYZ”
Playlist Results for USERXYZ

Zoom in one click at a time making your circle smaller and smaller to see if the video is still in the circle. If search results disappears, its no longer in the circle. Using this method you could take the search down to a city block or so. Then you can switch to http://maps.google.com/ and enable the YouTube overlay. You may find the video is places directly on top of the house where it was uploaded. But a video overlay only appeared in 1 out of the 6 times that I tried to narrow down to a street. It looks like the youtube overlays of Google maps doesn't have as much data as the map search on youtube.

I tested it with 3 video’s where I knew the target street address and in all 3 cases was able to locate their street. In one of the three cases the Google maps overlay displayed the YouTube video on top of the correct house. In one other case I narrowed down a video to a street, but when I asked the account owner about the address he had no idea how that address related to his video although it was within a few miles of his house. There were several cases where I couldn’t get YouTube to return any Geo-encoded video’s on that users account. Its not science, but here is some interesting data being revealed by that search.

UPDATE 1-31:  It appears that in the test case where the video led me to a strange location several miles from the account owners home, the video may have been tagged to the geographic center of the zip code of  the uploader.   This is going to be a significant stumbling block for any open source youtube geotagging missile guidances system projects resulting from this ground breaking research.



Popular posts from this blog

Awesome Keyboard Tricks - Clevo/Sager Backlight control from Powershell

I'm back on Windows.   After 8 years on a Macintosh I just couldn't go another day with ONLY 16GB of RAM.   I priced it out and for the cost of a top of the line MacBook I could get a tricked out PC with 32GB of ram and 2.5 TB or hard drive space (1.5 of it being SSD).   So I made the switch.  To get a top performing laptop I ended up buying a gaming machine from xoticpc.com.   The model is Sager NP9752 ( Clevo P750ZM ).    I have to say I like it quite a bit.    One of the features I was curious about was the "Programmable backlit keyboard".   With it you can set your keyboard backlight to various colors and light movement patterns.    Now, when I hear "programmable" I think APIs.   I was a little disappointed to find out there weren't any documented APIs that I could use to control the keyboard.    Your only choice is to use their built in tool to configure the lights on the keyboard.   That stinks.  I want to be able to change key colors automatically

SRUM-DUMP and SRUM_DUMP_CSV Ported to Python 3

SRUM_DUMP and SRUM_DUMP_CSV have been ported to Python3 and are available for download from the PYTHON3 branch of my github page. https://github.com/MarkBaggett/srum-dump/tree/python3 In moving to Python3 I also updated the modules that I depend upon to parse and create XLSX files and access the ESE database that contains the SRUM data.  I hope that this will fix the issue that some users have experienced with SRUDB.dat files that create very large spreadsheets.  If it does not please let me know and continue to use SRUM_DUMP_CSV.EXE to avoid the XLSX problem. In moving to Python3 you will find the process to be faster. If you would like to run the tools from source instructions for doing so are in the README on the github page.

New tool Freq_sort.py

I read an article on Fireeye's website the other day where they used Machine Learning to eliminate a lot of the noise that comes out of tools like strings.  It's pretty interesting and looks like it would save me some time when looking through malware. https://www.fireeye.com/blog/threat-research/2019/05/learning-to-rank-strings-output-for-speedier-malware-analysis.html I wondered how effective freq.py scores would be in helping to eliminate the noise.  45 minutes and 29 lines of Python code later I have something that looks like it works.  Check out freq_sort.py. Before freq_sort.py here is the output of strings on a piece of malware: student@573:~/freq$ strings -n 6 malware.exe | head -n 20 !This program cannot be run in DOS mode. e!Rich `.rdata @.data .pdata @.gfids @.rsrc @.reloc \$0u"H L$ SVWH K SVWH |$ H;_ <bt%<xt!<Zt |$ AVH l$ VWAV L$ SUVWH UVWATAUAVAWH 0A_A^A]A\_^] UVWATAUAVAWH @A_A^A]A\_^] After freq_sort.py the useful stings quickly bubble to t

Security Onion getting the most from Freq.py and Domain States

My talk at Security Onion conference has been posted and is available for viewing here.

SRUM DUMP and SRUM DUMP CSV Updated

An issue was reported where is some conditions SRUM_DUMP would stop processing and print the following error to the screen. UnboundLocalError: local variable 'sid_str' referenced before assignment The issue was that sometimes the SRUM database had entries in it that were all zeros. OrderedDict([('IdType', 3), ('IdIndex', 38127), ('IdBlob', '0000000000000000')]) I've released an update that handles the anomoly althought I do not understand the circomstances of why Windows would record all zero's for as the user SID. The issue was fixed and new versions of both SRUM DUMP and SRUM DUMP CSV were released.