After successfully participating in GSoC between 2009 and 2016, and having created or extended many honeynet technologies that have since gone on to become industry standard tools, we are very happy to annouce that The Honeynet Project has applied to be a mentoring organization once again in GSoC 2017.

This year sees significant changes in the GSoC program with a new payment model and an added third evaluation! We are excited about those changes and very curious to see how they play out. In addition to the program changes at Google, we are also trying out some ways to improve our students’ experience. Most notably, all our potential mentors have already joined the new GSoC Slack channel and we are excited to talk to you there!

Thug is a client honeypot that emulates a real web browser, fetches and executes any internal or external JavaScript, follows all redirects, downloadable files just like any browser would do, and collects the results in a mongodb collection. The purpose of this tool is to study, analyse and locate exploit kits and malicious websites. Thug’s analysis can be difficult to navigate or understand and this is where Rumal comes in. Rumal’s function is to be Thug’s GUI, providing users with trees, graphs, maps, tables and intuitive representations of Thug’s data.

CuckooML is a GSOC 2016 project by Kacper Sokol that aims to deliver the possibility to find similarities between malware samples based on static and dynamic analysis features of binaries submitted to Cuckoo Sandbox. By using anomaly detection techniques, such mechanism is able to cluster and identify new types of malware and can constitute an invaluable tool for security researchers.

It’s all about data..

Malware datasets tend to be relatively large and sparse. They are mostly made of categorical and string data, hence there is a strong need for good feature extraction approaches to obtain numerical vectors that can be feed into machine learning algorithms [e.g. Back to the Future: Malware Detection with Temporally Consistent Labels; Miller B., et al.]. Another common problem is concept drift, the continuous variation of malware statistical properties caused by never ending arms race between malware and antivirus developers. Unfortunately, this makes fitting the clusters even harder and requires the chosen approach to be either easy to re-train or be adaptable to the drift, with the latter option being more desirable.

At the end of February we were very happy to announce that The Honeynet Project had once again been selected to be a mentoring organization in Google Summer of Code (GSoC) 2016.

Since then, there as been a flurry of activity: GSoC 2016 student applications opened on March 14th at 19:00 UTC and closed on March 25th at 19:00 UTC. We received 54 student project applications, and our 24 mentors and org admins were hard at work in the following weeks assessing them and the students who applied. This is always the busiest part of GSoC for us, and with some significant changes to the GSoC program and supporting systems we suspected it might be one of the busiest times ever. But in fact, this year actually went pretty smoothly for us. We actually faced fewer difficult selection decisions than normal, which was a bit of a surprise.

As I blogged two weeks ago, after some great student projects between 2009 and 2015, The Honeynet Project had applied again this year to be a mentoring organization in Google Summer of Code (GSoC) 2016.

After a few anxious days of waiting, Google today published the official list of accepted GSoC 2016 mentoring orgizations. The great news if that we have been accepted once again. Hurrah!

GSoC 2016 student applications open on March 14th at 19:00 UTC and close on March 25th at 19:00 UTC.

Hi there, my name is Li Yuanchun and I’m glad to introduce DroidBot, a tool to improve the coverage of dynamic analysis.
As it is the case for malware targeting the desktop, static and dynamic analysis are also used for detection of Android malware. However, existing static analysis tools such as FlowDroid or DroidSafe lack accuracy because of specific characteristics of the Android framework like ICC (Inter-Component Communication), dynamic loading, alias, etc.  While dynamic analysis is more reliable because it executes the target app in a real Android environment and monitors the behaviors during runtime, its effectiveness relays on the amount of code it is able to execute, this is, its *coverage*. Because some malicious behaviors only appear at certain states, the more states covered, the more malicious behaviors detected. The goal of DroidBot is to help achieving a higher coverage in automated dynamic analysis. In particular, DroidBox works like a robot interacting with the target app and tries to trigger as many malicious behaviors as possible.
The Android official tool for this kind of analysis used to be  Monkey, which behaves similarly by generating pseudo-random streams of user events like clicks,touches, or gestures, as well as a number of system-level events. However, Monkey interacts with an Android app pretty much like its name indicates and lacks any context or semantics of the views (icons, buttons, etc.) in each app.

What is dpkt?

dpkt is a Python library that helps with “fast, simple packet creation/parsing, with definitions for the basic TCP/IP protocols”. It supports a lot of protocols (currently about 63) and has been increasingly used in a lot of network security projects. It is 44x faster than Scapy2, and 5x faster than Impacket3. With Scapy no longer in development, dpkt is the only network creation/parsing library for Python that is active.

As you may know, Thug is a handy tool for studying exploit kits, as it emulates a real browser complete of a set of plugins like Adobe Reader, Flash and Java. When you feed Thug with the URL of a suspicious web page, it “crawls” it and starts fetching and executing any internal or external JavaScript, following redirects and downloading files just like a browser would do. When Thug encounters some files it cannot analyze by itself (like Flash, Java and PDF), it passes them to external tools. Thug’s results are then collected in a variety of formats, with the default one being a set of collections inside a MongoDB database. Thug works very well but the output can be challenging to navigate, the result often being the ability to only check if the exploit kit’s payload (e.g. an *.exe file) has been downloaded: if not, one may think that the URL is not malicious, or maybe that the exploit kit is dead. That’s where a web GUI would come handy, and that’s exactly what Thug’s Rumal was born for: there’s plenty of information that can be extracted from Thug’s output and that can help a correct analysis to determine the maliciousness of a web page.

peepdf is a Python tool to explore PDF files in order to find out if the file can be harmful or not. The aim of this tool is to provide all the necessary components that a security researcher could need in a PDF analysis without using 3 or 4 tools to make all the tasks. With peepdf it’s possible to see all the objects in the document showing the suspicious elements, supports the most used filters and encodings, it can parse different versions of a file, object streams and encrypted files. With the installation of PyV8 and Pylibemu it provides Javascript and shellcode analysis wrappers too. Apart of this it is able to create new PDF files, modify existing ones and obfuscate them.

Although it is still winter in much of the northern hemisphere, for students and open source software developers, the gradually lengthing days mean that spring will soon be with us - and of course that means another chance to potentially get involved in Google Summer of Code (GSoC).

After successfully participating in GSoC between 2009 and 2015, and having created or extended many honeynet technologies that have since gone on to become industry standard tools, we are very happy to annouce that The Honeynet Project has applied to be a mentoring organization once again in GSoC 2016.