Category: Puzzle #3

Puzzle #3 Winners

At last, the long-awaited Puzzle #3 winners! Thank you all for your terrific submissions, and your patience as we tested each one carefully. Congratulations to everyone who sent in the correct answers.

As always, we were tremendously impressed by the quality of the entries. We received a wide variety of creative, original submissions, including file carving tools, network-layer tools, HTTP, XML and Plist analysis tools, graphical tools, command-line tools, and more. It was very hard to narrow down a winner, and there were several production-quality tools which will now be covered in future SANS “Network Forensics” curriculum. Please check out all the Finalist submissions!

The winner is… Matt Sabourin, for his elegant tool, ““. Matt’s tool is simple to use. It parses a pcap and creates a report for each potential AppleTV client, containing “Search Terms Sent by Client,” “Movie Items Viewed by Client,” “Overview of Recognized Requests,” and more. It also creates an overview report for all clients. Each of these reports can easily be included in the appendix of a professional forensics report. We could definitely envision using this in a real forensics case to quickly summarize AppleTV usage information. Congratulations, Matt! Your AppleTV is on it’s way.

We’d also like to call attention to several other submissions (in no particular order):

Amar Yousif created two excellent tools: applejuice and gzippedNOT. Amar’s “gzippedNOT” parses gzipped content out of HTTP responses. This tool will be AWESOME for squid proxy analysis as well. 🙂 “Applejuice” dumps out the list of search queries for each AppleTV IP address. “Applejuice” also wins the Best Name Award!

Richard Springs built two great tools: transmute.rb and scarabsieve.rb. Scarabsieve parses through any Webscarab-logged traffic, carves it all out, dumps it into a directory, and prints MD5 and SHA1 hashes for each carved file. This script alone is very useful for any WebScarab user. Richard also wrote “transmute.rb” to convert any pcap into the WebScarab log format so that scarabsieve can parse it. Wow! Nice work.

Sébastien Damaye built a tool called “” to extract all the files in the packet capture and list out the search requests. This tool even goes a step above and automatically creates a graphical web interface which you can scroll through to view all the files. He also submitted a companion tool,, which pulls AppleTV searches out of the packet capture and prints them out. Sébastien included a *fantastic* writeup which everybody should read. We were really impressed.

Franck Guénichot lived up to his reputation as network forensics hacker extraordinare with his excellent tool, “httpdumper.” This tool displays HTTP conversations, filters and dumps the contents (automatically decompressing gzipped content). Franck also submitted two handy tools, macfinder.rb, and plist.rb. Franck’s writeup is very thorough– definitely check it out for a great walk-through of the solutions.

Tom Samstag wrote a really cool tool, httpAnalyzer, which creates a graphical web interface that lets you browse through HTTP traffic. It includes MD5 and SHA1 hashes of each file contained in the packet capture. The interface is very user-friendly! Tom’s httpAnalyzer is easily extensible, and we hope we’ll see it again in future contests.(Note: When you load the page, httpAnalyzer makes a request to, apparently in order to get up-to-date jQuery Javascript library. If you are using it for forensics work, you’ll want to block outbound traffic.) Tom also wrote a very handy tool called “,” which analyzes a pcap and reports basic info such as a packet count, MAC addresses and IP addresses.

Lou Arminio built a Plist parser to analyze Apple plist files, as well as an HTTP analyzer called “httpparse”. On top of that, he created a great tool called pcaputil which analyzes TCP flows and carves files out of selected TCP flows and creates MD5sums. These are three handy little tools. Nice work!

Michael_Nijs built upon an open-source pcap analysis tool,, adding the option to parse GET and POST requests and display the values of any parameter in the URL. We appreciated that he leveraged existing code and built a useful extension.

Alan Tu wrote a script,, which leverages tshark’s powerful HTTP dissection capability, outputs handy information to a file, and can also produce filtered pcaps. Alan also wrote an HTTP response extractor,, and polished his TCP stream analysis tool, Check them out!

Wesley McGrew wrote an excellent tool, “,” which carves out plist files and creates a CSV file with useful information about AppleTV traffic from a pcap. The tool is very easy to use, and a great foundation for detailed forensic analysis. His writeup is outstanding, too– read about how he identified six request types from the pcap file, and incorporated these into’s output.

These tools are great! Thank you all for making your work available to the community. We hope you’ll continue to maintain and extend your code.

Many thanks to everyone who participated. We hope to see you guys in future contests.


Matt Sabourin
(Wins Ann’s Apple TV!)


Alan Tu
Amar Yousif
Franck Guénichot
Lou Arminio
Michael Nijs
Richard Springs
Sébastien Damaye
Tom Samstag
Wesley McGrew


Alan Reed
Davis Stovall
Eric Kollmann
Erik Barker
Felix AIME
Jeremy Impson
Joe Creasey
Juha Lampinen
Stefan Pettersson

Correct Answers:

Ahmed Adel Mohamed
Alan Reed
Alan Tu
Amar Yousif
Andrew Brandt
Andrew Scharlott
Chen Jung Weng
Chris Steenkamp
Daniel Dickerman
Eric Kollmann
Erik Barker
Félix AIME
Franck Guénichot
Halil Ozgur BAKTIR
James O. Holley
Jeremy D. Impson
Joe Creasey
Jon Cook
Juha Lampinen
Karthikeyan C Kasiviswanathan
Lou Arminio
Marc Quibell
Masashi Fujiwara
Matt Sabourin
Michael Nijs
Mohammad Zeyad Kebreteh
Nicholas Albright
Peter Chong
Richard Springs
Russ Klanke
Sebastien DAMAYE
Sébastien Duquette
Tareq Saade
Tim Naami
Tom Samstag
Wesley McGrew
Winter Faulk

Hint for Ann’s AppleTV

Just wanted to send a hint out for those of you who are out to win Ann’s AppleTV.

We’ve received lots of submissions with the correct answer, but to win the AppleTV, you’ll need to go a step beyond manual extraction with Wireshark or Network Miner. Imagine if you had a huge packet capture containing LOTS of AppleTV traffic. There’s no way you could extract that manually!

Can you build a tool that will automatically list each of the movies that a user previewed? Or all of the terms that Ann searched for? Carve out files transferred and their MD5sums? Even perhaps reconstruct what Ann saw on the AppleTV based on the traffic content?

To win the AppleTV, you’ll need to be creative and take things to a level beyond just manual extraction. (By the way, we suspect that the underlying traffic for the AppleTV is the same format as iTunes traffic.)

Submissions are due by the end of 2/1/10 (next Monday night). Good luck!!

Ann’s AppleTV

Ann and Mr. X have set up their new base of operations. While waiting for the extradition paperwork to go through, you and your team of investigators covertly monitor her activity. Recently, Ann got a brand new AppleTV, and configured it with the static IP address Here is the packet capture with her latest activity.

You are the forensic investigator. Your mission is to find out what Ann searched for, build a profile of her interests, and recover evidence including:

1. What is the MAC address of Ann’s AppleTV?
2. What User-Agent string did Ann’s AppleTV use in HTTP requests?
3. What were Ann’s first four search terms on the AppleTV (all incremental searches count)?
4. What was the title of the first movie Ann clicked on?
5. What was the full URL to the movie trailer (defined by “preview-url”)?
6. What was the title of the second movie Ann clicked on?
7. What was the price to buy it (defined by “price-display”)?
8. What was the last full term Ann searched for?

Prize: Ann’s AppleTV (of course!)

Deadline is 2/01/10 (11:59:59PM UTC-11) (In other words, if it’s still 2/01/10 anywhere in the world, you can submit your entry.)

Please use the Official Submission form to submit your answers. Here is your evidence file:
MD5 (evidence03.pcap) = f8a01fbe84ef960d7cbd793e0c52a6c9

The MOST ELEGANT solution wins. In the event of a tie, the entry submitted first will receive the prize. Coding is always encouraged. We love to see well-written, easy-to-use tools which automate even small sections of the evidence recovery. Graphical and command-line tools are all eligible. You are welcome to build upon the work of others, as long as their work has been released under a GPL license. (If it has been released under another free-software license, email us to confirm eligibility.) All responses should be submitted as plain text. Microsoft Word documents, PDFs, etc will NOT be reviewed.

Feel free to collaborate with other people and discuss ideas back and forth. You can even submit as a team (there will be only one prize). However, please do not publish the answers before the deadline, or you (and your team) will be automatically disqualified. Also, please understand that the contest materials are copyrighted and that we’re offering them publicly for the community to enjoy. You are welcome to publish full solutions after the deadline, but please use proper attributions and link back. If you are interested in using the contest materials for other purposes, just ask first.

Exceptional solutions may be incorporated into the SANS Network Forensics Toolkit. Authors agree that their code submissions will be freely published under the GPL license, in order to further the state of network forensics knowledge. Exceptional submissions may also be used as examples and tools in the Network Forensics class. All authors will receive full credit for their work.

Deadline is 2/01/10 (11:59:59PM UTC-11). Here’s the Official Submission form. Good luck!!

Copyright 2009, Lake Missoula Group, LLC. All rights reserved.