honeynets

The first challenge of the Honeynet Forensic Challenge 2010 has been posted at http://honeynet.org/node/504. The task is to analyze a packet capture that was collected by a honeypot. Analyze and answer the following questions:

1. Which systems (i.e. IP addresses) are involved? (2pts)
2. What can you find out about the attacking host (e.g., where is it located)? (2pts)
3. How many TCP sessions are contained in the dump file? (2pts)
4. How long did it take to perform the attack? (2pts)
5. Which operating system was targeted by the attack? And which service? Which vulnerability? (6pts)
6. Can you sketch an overview of the general actions performed by the attacker? (6pts)
7. What specific vulnerability was attacked? (2pts)
8. What actions does the shellcode perform? Pls list the shellcode. (8pts)
9. Do you think a Honeypot was used to pose as a vulnerable victim? Why? (6pts)
10. Was there malware involved? Whats the name of the malware? (We are not looking for a detailed malware analysis for this challenge) (2pts)
11. Do you think this is a manual or an automated attack? Why? (2pts)

Get the pcap at http://honeynet.org/files/attack-trace.pcap_.gz, they were provided together with the questions by Tillmann Werner. Deadline for submissions is Monday, February 1st 2010 at 17:00 EST. There will be some small prizes, among them signed copies of our book "Virtual Honeypots: From Botnet Tracking to Intrusion Detection". Full information is available at http://honeynet.org/node/504.

Finally, after several years without any Honeynet Project Challenges, there will finally be new Forensic Challenges starting next Monday (January 18th, 2010). Here is the official announcement:

I am very happy to announce the Honeynet Project Forensic Challenge 2010. The purpose of the Forensic Challenges is to take learning one step farther. Instead of having the Honeynet Project analyze attacks and share their findings, Forensic Challenges give the security community the opportunity to analyze attacks and share their findings. In the end, individuals and organizations not only learn about threats, but also learn how to analyze them. Even better, individuals can access the write-ups from other individuals, and learn about new tools and techniques for analyzing attacks. Best of all, the attacks of the Forensic Challenge are attacks encountered in the wild, real hacks, provided by our members.
It has been several years since we provided Forensic Challenges and with the Forensic Challenge 2010, we will provide desperately needed upgrades. The Forensic Challenge 2010 will include a mixture of server-side attacks on the latest operating systems and services, attacks on client-side attacks that emerged in the past few years, attacks on VoiP systems, web applications, etc. At the end of challenge, we will provide a sample solution created by our members using the state-of-the-art tools that are publicly available, such as libemu and dionaea.
The first challenge (of several for 2010) will be posted on our Forensic Challenges web site on Monday, January 18th 2010. We will be open to submissions for about two weeks and announce the winners by February 15th 2010. This year, we will also award the top three submissions with prizes! Please check the web site on Monday, January 18th 2010 for further details….

Christian Seifert

Full details will be published at http://honeynet.org/challenges.

Update: The date was apparently wrong, I corrected it from January 15th to January 18th.

A new series of papers is available from the Honeynet Project: "Know Your Tools" deals with specific types of honeypots and explains how to use them. The first paper in this series deals with Picviz, a tool to visualize data based on parallel coordinates plots.

Picviz is a parallel coordinates plotter which enables easy scripting from various input (tcpdump, syslog, iptables logs, apache logs, etc..) to visualize data and discover interesting aspects of that data quickly. Picviz uncovers previously hidden data that is difficult to identify with traditional analysis methods.

The paper is available at http://www.honeynet.org/node/499".

Abstract:
This document explains how Picviz can be used to spot attacks. We will use three examples in this paper; analysis of ssh connection logs, demonstration of the graphical interface on network data generated by a port scanner and the use of Picviz command line to discover attacks towards an Apache web server. Picviz can handle large amounts of data, as illustrated by the last example in which two years of raw Apache access logs are analyzed. We will show how we can find attacks that previously have been hidden and discover them in a very short time!
We hope Picviz will make you more efficient in analyzing any kind of log files, including network traffic, and able to spot abnormalities even with large dataset.

Here an announcement regarding the end of GSoC'09:

Web sites are hacked all the time. Web application, database, and cross-site scripting vulnerabilities expose a large attack surface that can be exploited to, among others, deface the web site, send spam, convert web site into bots, and serve drive-by-download attacks. Glastopf is a low-interaction honeypot that emulates a vulnerable web server hosting many web pages and web applications with thousands of vulnerabilities. Glastopf is easy to setup and once indexed by search engines, attacks will pour in by the thousands daily. Glastopf has been developed as part of the 2009 Google of Summer Code by student Lukas Rist (and mentored by me). It can be downloaded from the Glastopf trac site at http://trac.glastopf.org/trac. More information on Glastopf can be found on the project site at http://glastopf.org/.

A common technique employed by spammers is to send spam mails with the help of botnets. In a typical setting, the spammer uses so called template-based spamming: the attacker sends the bots a spam template that describes the structure of the spam message to be sent. Furthermore, the attacker sends meta-data like recipient list, subject list, and a list of URLs that are used to fill in variables in the template. The bots then construct an email based on the template and the meta-data, and send this email to the targets. As a result, the actual work of handling the SMTP communication is moved from the control server to the bots. Nowadays this technique is used by most large spam botnets, like Waledac, Bobax, Rustock, Cutwail, and a lot of the other major spam botnets as Joe Stewart explained in detail.

Since spammers nowadays use such a tactic, we can also collect spam mails in a more efficient way: Instead of waiting at the end-user's mailboxes or spamtraps for mail messages to arrive and then decide whether or not this is spam, we directly interact with the servers that are used to send spam messages. The basic idea is that we execute spambots, i.e., malicious software dedicated to sending spam emails, in a controlled (honeypot) environment and collect all email messages sent by the bots. This enables us to directly interfere with botnet control servers to collect current spam messages sent by a specific botnet.

We describe this idea in more detail in a short paper that was published at DIMVA'09. The paper is also available on this blog.

Abstract: With increasing security measures in network services, remote exploitation is getting harder. As a result, attackers concentrate on more reliable attack vectors like email: victims are infected using either malicious attachments or links leading to malicious websites. Therefore efficient filtering and blocking methods for spam messages are needed. Unfortunately, most spam filtering solutions proposed so far are reactive, they require a large amount of both ham and spam messages to efficiently generate rules to differentiate between both. In this paper, we introduce a more proactive approach that allows us to directly collect spam message by interacting with the spam botnet controllers. We are able to observe current spam runs and obtain a copy of latest spam messages in a fast and efficient way. Based on the collected information we are able to generate templates that represent a concise summary of a spam run. The collected data can then be used to improve current spam filtering techniques and develop new venues to efficiently filter mails.