Talent gap in security

Screenshot Github repository

There are a whole bunch of articles outlining the talent gap in security related positions. More and more jobs require IT skills and IT systems are more and more integrated in all areas of our life with an dramatic increase of open positions in security and privacy.

People living in areas like SF / silicon valley, New York or Zurich can find easily new jobs within days, but those locations are also very expensive and some companies can not hire there.

There is a good opportunity to fight the talent gap: hiring remote

This post is not to outline the benefits of shortcomings of working / hiring remote but the fact that it is very hard for candidates to find companies welcoming remote security minded people.

On the other side, companies have a hard job, market themselves against the big brands to attract remote people.

That combined is the reason I created yet another list on github, called companies-hiring-security-remote. It is a curated list and open for issues / pull requests to act as a platform for job seeking people and companies to give them a little more visibility.

I really hope that this will help people and I am happy to receive feedback.

Link to the repository: https://github.com/deralexxx/companies-hiring-security-remote

Bitcoin transaction in timelines

Investigation bad people might involve bitcoin, the blockchain technology is very popular among criminals, as it is easy to use and „untraceable“ [1]. E.g. in most ransomware cases like „Ryuk“ [2] the company Crowdstrike has listed several bitcoin wallets, that they attribute to the threat actor.

How can that information help your investigation / your intelligence gathering? IN certain ways, you could track your own wallets for transactions to these wallets. Another aspect, that this blogpost will cover on is the timeline aspect of it.

As bitcoin transactions make use of the blockchain, who is public by design, it is possible to:

  • tell, how many bitcoins a certain wallet currently holds
  • see transactions from the past

The second aspect is what I want to focus on, because if we have a look at the transactions, we might be able to identify the point in time a certain group was active and enhance our other DFIR activities enriched with that information. The transaction log is like your journal of your bank account, it tells basically who is transferring money to a wallet and where the bitcoins are transferred to.

In the example above, the bitcoin wallets we are interested in are (Source Crowdstrike Blog post):

BTC AddressTotal ReceivedNo ReceivedTotal Value (USD)

Source of transaction information

There is a whole bunch of public webpages who give transaction history for a given wallet, but as it should be an automated step, the goal is to have a page with an API, after some searching I found: https://chain.so/api .

Making the call

Doing the API call to get transaction information is pretty simple:

GET /api/v2/address/{NETWORK}/{ADDRESS} 

That will give you the following information

  "status": "success",
  "data": {
    "network": "DOGE",
    "address": "DM7Yo7YqPtgMsGgphX9RAZFXFhu6Kd6JTT",
    "balance": "31.03885339",
    "received_value": "25828731.93733507",
    "pending_value": "0.0",
    "total_txs": 225,
    "txs": [ ... ]

Which is exactly what we need, with some Python JSON parsing, it is easy to get the info we want – the code I am using is available on https://github.com/deralexxx/osint_to_timesketch

After that we have an CSV with the date, the transaction happened, the raw information from the API and some meta data, enough to bake into a timeline.


The script is already made to output CSV files ready for importing them into Timesketch, as I found it to be the ideal tool to work with data points related to timestamps. Importing the CSV is straight forward and explained in the official documentation page [3].

The timeline csv looks like the following:

CSV of BTC history

Making it pretty

Importing it into Timesketch, the timeline looks very nice:

BTC transactions in Timesketch

Added Value

Now what is the added value for investigations? The above is another layer of data points /evidence. It can be used to weight limit findings in your organisation, e.g. you assume you are hit by a phishing campaign, if your phishing campaign was seen a lot earlier or a lot later than the transactions above display, it is unlikely you are hit by the same campaign. It can also be used to make a case against individuals if enriched by host forensics – your imagination is the limit.


I hope the article is helpful and the scripts can be used, let me know via comments within the blog, issues on github or twitter messages https://twitter.com/alexanderjaeger if you have any questions, improvements.

Thx for reading

Further reading / references

  • [1] http://www.sciencemag.org/news/2016/03/why-criminals-cant-hide-behind-bitcoin
  • [2] https://www.crowdstrike.com/blog/big-game-hunting-with-ryuk-another-lucrative-targeted-ransomware/
  • [3] https://github.com/google/timesketch/blob/master/docs/CreateTimelineFromJSONorCSV.md

Autotimeliner to CyberChef to Timesketch

As you might know, I love to combine several OpenSource tools to get things done. One thing I wanted to play for some weeks is Autotimeliner by Andrea Fortuna.This tool is made to extract events from an Memory Image to combine it into a timeline. If you have a timeline, what comes next? Of course, putting it into Timesketch. So let’s give it a try.

We start with a memory dump from a Stuxnet infection from https://github.com/ganboing/malwarecookbook. Download the four files, extract them and you are good to go.



Installation is pretty easy, install Volatility either via pre-compiled binary or install it manually, see the Volatility installation wiki for further information.

Test it running:

vol.py -v


To install sleuthkit run:

brew install sleuthkit


sudo apt-get install sleuthkit

Installation Autotimeliner

Simply clone the GitHub repository:

git clone https://github.com/andreafortuna/autotimeliner.git

Run it

python autotimeline.py -f /Users/foobar/Downloads/stuxnet.vmem.zip/stuxnet.vmem -p WinXPSP2x86 -t 2009-10-20..2018-10-21

That might take some time depending on your hardware.

Now you have an csv file around 5.6 MB.

                _     _______ _                _ _
     /\        | |   |__   __(_)              | (_)
    /  \  _   _| |_ ___ | |   _ _ __ ___   ___| |_ _ __   ___ _ __
   / /\ \| | | | __/ _ \| |  | | '_ ` _ \ / _ \ | | '_ \ / _ \ '__|
  / ____ \ |_| | || (_) | |  | | | | | | |  __/ | | | | |  __/ |
 /_/    \_\__,_|\__\___/|_|  |_|_| |_| |_|\___|_|_|_| |_|\___|_|

- Automagically extract forensic timeline from volatile memory dump -

Andrea Fortuna - andrea@andreafortuna.org - https://www.andreafortuna.org

*** Processing image /Users/foobar/Downloads/stuxnet.vmem.zip/stuxnet.vmem
*** Using custom profile: WinXPSP2x86
*** Creating memory timeline......done!
*** Creating shellbags timeline......done!
*** Creating $MFT timeline......done!
*** Merging and filtering timelines......done!
Timeline saved in /Users/foobar/Downloads/stuxnet.vmem.zip/stuxnet.vmem-timeline.csv

The format used for the dates is not compatible with Timesketch:

more /Users/foobar/Downloads/stuxnet.vmem.zip/stuxnet.vmem-timeline.csv
Date,Size,Type,Mode,UID,GID,Meta,File Name
Tue Oct 20 2009 12:08:04,0,ma.b,---a-----------,0,0,84995,"[MFT STD_INFO] Python26\Lib\SITE-P~1\setuptools-0.6c11-py2.6.egg-info\TOP_LE~1.TXT (Offset: 0x8a28c00)"
Tue Oct 20 2009 12:08:04,0,ma.b,---a-----------,0,0,85000,"[MFT STD_INFO] Python26\Lib\SITE-P~1\SETUPT~1.EGG\DEPEND~1.TXT (Offset: 0x75e4000)"
Tue Oct 20 2009 12:08:06,0,m..b,---a-----------,0,0,84985,"[MFT STD_INFO] Python26\Scripts\EASY_I~1.PY (Offset: 0x91b9400)"
Tue Oct 20 2009 12:08:06,0,ma.b,---a-----------,0,0,84986,"[MFT STD_INFO] Python26\Scripts\EASY_I~1.MAN (Offset: 0x91b9800)"
Tue Oct 20 2009 12:08:06,0,ma.b,---a-----------,0,0,84987,"[MFT STD_INFO] Python26\Scripts\EASY_I~1.EXE (Offset: 0x91b9c00)"
Tue Oct 20 2009 12:08:06,0,ma.b,---a-----------,0,0,84988,"[MFT STD_INFO] Python26\Scripts\EASY_I~2.MAN (Offset: 0x1042f000)"
Tue Oct 20 2009 12:08:06,0,m..b,---a-----------,0,0,84989,"[MFT STD_INFO] Python26\Scripts\EASY_I~2.PY (Offset: 0x1042f400)"
Tue Oct 20 2009 12:08:06,0,ma.b,---a-----------,0,0,84990,"[MFT STD_INFO] Python26\Scripts\EASY_I~2.EXE (Offset: 0x1042f800)"
Tue Oct 20 2009 21:21:26,0,...b,---a-----------,0,0,66083,"[MFT STD_INFO] Documents and Settings\Administrator\Desktop\SysinternalsSuite\ZoomIt.exe (Offset: 0x1a8a5c00)"
Wed Oct 21 2009 00:02:28,76800,m...,---a-----------,0,0,65342,"[MFT FILE_NAME] Program Files\NTCore\Explorer Suite\Tools\DRIVER~1.EXE (Offset: 0x14b9c800)"
Wed Oct 21 2009 00:02:28,76800,m...,---a-----------,0,0,65342,"[MFT FILE_NAME] Program Files\NTCore\Explorer Suite\Tools\DriverList.exe (Offset: 0x14b9c800)"
Wed Oct 21 2009 00:02:28,76800,m...,---a-----------,0,0,65342,"[MFT STD_INFO] Program Files\NTCore\Explorer Suite\Tools\DRIVER~1.EXE (Offset: 0x14b9c800)"
Wed Oct 21 2009 18:25:52,780800,m...,---a-----------,0,0,65338,"[MFT FILE_NAME] Program Files\NTCore\Explorer Suite\TASKEX~1.EXE (Offset: 0x14b1b800)"

so we need to adjust that. In the past, I used an own developed python script https://github.com/deralexxx/timesketch-tools/tree/master/date_converter for that, but that does not really scale, so I considered another option.


An open source tool by GCHQ: https://gchq.github.io/CyberChef/

A simple, intuitive web app for analysing and decoding data without having to deal with complex tools or programming languages. CyberChef encourages both technical and non-technical people to explore data formats, encryption and compression.



git clone https://github.com/gchq/CyberChef


Now open it

From the CSV that was generated, use your favourite tool to extract the first column of the csv which should look like that:

Tue Oct 20 2009 12:08:04
Tue Oct 20 2009 12:08:04
Tue Oct 20 2009 12:08:06
Tue Oct 20 2009 12:08:06
Tue Oct 20 2009 12:08:06
Tue Oct 20 2009 12:08:06
Tue Oct 20 2009 12:08:06
Tue Oct 20 2009 12:08:06
Tue Oct 20 2009 21:21:26
Wed Oct 21 2009 00:02:28

Now use the following CyberChef Recipe


And paste them all into input. It will result in a file you can download with the output.

Now the output txt has two CSV columns, you need to combine them with your autotimeliner csv to have the following headers:

datetime	timestamp	timestamp_desc
2009-10-20T12:08:04+0000	1256040484000	stuxnet.vmem_Mem_Dump_Timeline
2009-10-20T12:08:04+0000	1256040484000	stuxnet.vmem_Mem_Dump_Timeline
2009-10-20T12:08:06+0000	1256040486000	stuxnet.vmem_Mem_Dump_Timeline
2009-10-20T12:08:06+0000	1256040486000	stuxnet.vmem_Mem_Dump_Timeline
2009-10-20T12:08:06+0000	1256040486000	stuxnet.vmem_Mem_Dump_Timeline
2009-10-20T12:08:06+0000	1256040486000	stuxnet.vmem_Mem_Dump_Timeline

Now the csv should like like:

more stuxnet.vmem.zip/stuxnet.vmem-timeline_timesketch.csv 

2009-10-20T12:08:04+0000,1256040484000,stuxnet.vmem_Mem_Dump_Timeline,Tue Oct 20 2009 12:08:04,0,ma.b,---a-----------,0,0,84995,[MFT STD_INFO] Python26\Lib\SITE-P~1\setuptools-0.6c11-py2.6.egg-info\TOP_LE~1.TXT (Offset: 0x8a28c00)
2009-10-20T12:08:04+0000,1256040484000,stuxnet.vmem_Mem_Dump_Timeline,Tue Oct 20 2009 12:08:04,0,ma.b,---a-----------,0,0,85000,[MFT STD_INFO] Python26\Lib\SITE-P~1\SETUPT~1.EGG\DEPEND~1.TXT (Offset: 0x75e4000)
2009-10-20T12:08:06+0000,1256040486000,stuxnet.vmem_Mem_Dump_Timeline,Tue Oct 20 2009 12:08:06,0,m..b,---a-----------,0,0,84985,[MFT STD_INFO] Python26\Scripts\EASY_I~1.PY (Offset: 0x91b9400)

There is one little caveat, you need to add „“ around the message, because some values might break the Import process.

That can now be imported into Timesketch

Et voila, a timesketched Memory Dump