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Folks the submission deadline for the Forensic Challenge 7 – “Forensic Analysis of a Compromised System” - put up by Hugo Gonzalez from the Mexico Chapter and Guillaume Arcas from the French Chapter - has passed. We have received 16 submissions and will be announcing results on Friday, Apr 29th 2011. The winners will get a copy of the book "Virtual Honeypots - From Botnet Tracking to Intrusion Detection" written by Niels Provos and Thorsten Holz.
UPDATE: Forensic Challenge 7 results will be announced on Friday, May 6th 2011.
The Honeynet Project
A new improvement in PHoneyC DOM emulation code was committed in SVN r1624. The idea is to better emulate the DOM behaviour depending on the selected browser personality. Let's take a look at the code starting from the personalities definition in config.py.
“Dionaea is meant to be a Nepenthes successor, embedding Python as scripting language, using libemu to detect shellcodes, supporting IPv6 and TLS” (taken from Dionaea homepage). Besides being the most interesting project for trapping malware exploiting vulnerabilities, Dionaea supports a really cool feature which allows it to log to XMPP services as described here. TIP now exploits this feature receiving and storing such logs (really thanks to Markus Koetter for his help and support).
A few weeks ago I started reviewing the PHoneyC DOM emulation code and realized it was turning to be hard to maintain and debug due to a huge amount of undocumented (and sometimes awful) hacks. For this reason I decided it was time to patch (and sometimes rewrite from scratch) such code. These posts will describe how the new DOM emulation code will work. The patch is not available right now since I'm testing the code but plans exists to commit it in the PHoneyC SVN in the next days.
Challenge 3 - Banking Troubles - (provided by Josh Smith and Matt Cote from The Rochester Institute of Technology Chapter, Angelo Dell'Aera from the Italian Chapter and Nicolas Collery from the Singapore Chapter) is to investigate a memory image of an infected virtual machine.
The challenge has been completed on May 12th 2010.
Skill Level: Difficult
Company X has contacted you to perform forensics work on a recent incident that occurred. One of their employees had received an email from a fellow co-worker that pointed to a PDF file. Upon opening, the employee did not seem to notice anything, however recently they have had unusual activity in their bank account. Company X was able to obtain a memory image of the employee’s virtual machine upon suspected infection. Company X wishes you to analyze the virtual memory and report on any suspected activities found. Questions can be found below to help in the formal report for the investigation.
hn_forensics.tgz Sha1: 8178921fd065ad2de9c6738fe062d2b37402c04a
Forensic_Challenge_3_-_Banking_Troubles_Solution.pdf - Sha1: 986752a9aa4b832951dfa6319cb5e16256a9b3c9
This work by Josh Smith, Matt Cote, Angelo Dell'Aera and Nicolas Collery is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
Challenge 1 - pcap attack trace - (provided by Tillmann Werner from the Giraffe Chapter) is to investigate a network attack.
Send submissions (please use the MS word submission template or the Open Office submission template) email@example.com no later then 17:00 EST, Monday, February 1st 2010. Results will be released on Monday, February 15th 2010. Small prizes will be awarded to the top three submissions.
Skill Level: Intermediate
A network trace with attack data is provided. (Note that the IP address of the victim has been changed to hide the true location.) Analyze and answer the following questions:
attack-trace.pcap_.gz Sha1: 0f5ddab19034b2656ec316875b527d9bff1f035f
Forensic Challenge 2010 - Scan 1 - Solution_final.pdf Sha1: 7482a4d020cddde845344f8b02e05012
This work by Tillmann Werner is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
What is TIP? TIP stands for Tracking Intelligence Project. In my most beautiful dreams, TIP should be an information gathering
framework whose purpose is to autonomously collect Internet threat
trends. It's entirely written in Python using Twisted and bound to the Django framework in order to abstract the underlying database and to easily build a web interface to the data.