honeyblog - malware

2011 Honeynet Project Security Workshop Slides + Videos

nospam@example.com (Thorsten Holz) — Tue, 26 Apr 2011 23:06:18 +0200

The slides and videos from the 2011 Honeynet Project Security Workshop (Paris) are now available! You can get the material from http://www.honeynet.org/SecurityWorkshops/2011_Paris.

About the workshop:

The workshop brought together experts in the field of information security from around the world to share the latest advances in security research. Our members covered topics such as new honeyclients, mobile malware, new reversing techniques, VOIP attacks and even social behavior of attackers. And besides the presentation, Felix Leder and Mark Schloesser from our Giraffe chapter and Guillaume Arcas from our French chapter put up some hands on exercises that allowed participants to test their skillz.

The Last Line of Defense - http://tllod.com

nospam@example.com (Thorsten Holz) — Thu, 01 Jul 2010 15:11:00 +0200

I am excited to announce that the website of our start-up company LastLine, Inc., is now live at http://www.tllod.com. The team behind LastLine is composed of people you know from the International Secure Systems Lab (http://iseclab.org), we are coming from the University of California, Santa Barbara, the Vienna University of Technology (Austria), Eurecom (France), and Ruhr-University Bochum (Germany). We all have extensive expertise in malware analysis and malware countermeasures (see our list of publications) and you might know tools like Anubis or Wepawet that have been developed by us.

LastLine, Inc., provides protection technology that is complementary to existing anti-virus software and firewalls. Our approach is based on cyber crime intelligence that we gather by analyzing millions of suspicious URLs and binaries each day. More precisely, using our advanced malware analysis tools, we pinpoint the exploit servers that are behind drive-by exploits campaigns and the command and control server that manage botnets. These servers constitute the malicious infrastructure that is used by cyber criminals to carry out their attacks.

One of the first product we offer is llweb, a tool that analyzes web sites for the presence of malicious code, such as drive-by download exploits. llweb was developed by the creators of Wepawet and you can find out more about the tool at http://tllod.com/products/llweb. We also offer several other tools and services: llmon is a service that helps organizations to determine if their hosts are used to deliver or control malware. We continuously monitor whether a customer's assets participate in malicious activities, and if so, we provide detailed and early warning so that proper mitigation steps can be initiated. llmon was developed by some of the creators of FIRE. Furthermore, we provide access to the list of IP addresses, domains, and URLs that we identify to be associated with malicious activity on the Internet. Customers can obtain continuously-updated intelligence, which can be leveraged internally to identify compromised hosts or configure network access control mechanisms. You can find more about our products at http://tllod.com/what.

Chaosradio Express #155

nospam@example.com (Thorsten Holz) — Thu, 10 Jun 2010 18:07:40 +0200

Recently I recorded a longer podcast together with Tim Pritlove on malware and botnets. It was published a few days ago as Chaosradio Express #155. The podcast is in German and lasts for about 2.5 hours. The podcast is available at http://chaosradio.ccc.de/cre155.html and you can also get it via iTunes.

Here the German description:

Malware hat sich in den letzten 10 Jahren von einem Forschungsfeld zu einer globalen Bedrohung der internationalen Dateninfrastruktur entwickelt. Botnetze stellen dabei die bedauerliche Krönung der kriminellen Aktivitäten dar und es erfordert einen großen Aufwand, diesen Systemen nachzugehen und sie wieder auszuschalten. Trotz eines fortwährenden Katz- und Mausspielchens gelingt es den Sicherheitsforschern immer wieder, große Botnetze vom Netz zu nehmen. Im Gespräch mit Tim Pritlove erläutert Thorsten Holz Geschichte und technische Hintergründe zu Malware und Botnetzen.

Themen: wie sich Malware über die Zeit vom Experiment zum Werkzeug von Kriminellen entwickelt hat; welche Sicherheitslücken ausgenutzt werden; welche Methoden Betriebssysteme haben, sich gegen Malware zu wehren; das Layer-8-Problem; die Antiviren-Industrie; was Microsoft für seine Sicherheit getan hat; Botnetze und Spam und andere Formen der Monetarisierung; wie sich Botnetze gegen Aufklärung schützen; wie man ein Botnetz ausforscht, austrickst und lahmlegt; Botnetze aufspüren mit Honeypots; Botnetze in Behörden und Botschaften; Kommunikation und Kollaboration von Securitygruppen; technische und moralische Probleme beim Herunterfahren eines Botnets; Kooperation mit ISPs; Botnetzbekämpfung vs. Zensurinfrastruktur; Botnetze und der Mac; Konzepte für sichere Betriebssysteme; Security Usability; Automatisierte Malware Analyse.

"Inspector Gadget: Automated Extraction of Proprietary Gadgets from Malware Binaries"

nospam@example.com (Thorsten Holz) — Fri, 12 Mar 2010 08:09:00 +0100

When analyzing malware samples, a human analyst is typically interested in understanding/recovering a specific algorithms of the given sample. In the case of Conficker, for example, she might be interested in extracting the domain generation algorithm such that she can understand what domains are currently and in the future used by the malware. Or for spam bots, she might be interested in how the malware downloads spam templates, decodes them, and then generates the actual spam messages. Or for bots, she might be interested in understanding how binary updates are downloaded, decoded, and then executed.

In each case, the binary itself encodes the algorithm, but it is cumbersome and hard work to understand all of this. Thus it would be useful to have a tool that enables a malware analyst to automatically extract from a given binary sample the relevant algorithm related to a specific task. In a paper that will be presented at the 31st IEEE Symposium on Security & Privacy we introduce Inspector Gadget, a tool that implements exactly this. A gadget encapsulates all code related to a specific task and can be executed in a stand-alone fashion. A gadget player can take a gadget and replay it, for example to determine which domains are currently used by Conficker, or download and decode an update for a bot binary. Furthermore, we introduce an approach to revert gadget based on a enhanced brute-force algorithm: this is useful to understand the effects of malware in detail and we can (in certain cases) also revert obfuscation algorithms, i.e., to understand what data has been exfiltrated by a given sample. The full paper has all the details and describes Inspector Gadget in more depth. And if you are interested in the topic, you should also read the paper by Caballero et al. on BCR (paper title is "Binary Code Extraction and Interface Identification for Security Applications").

Abstract:
Unfortunately, malicious software is still an unsolved problem and a major threat on the Internet. An important component in the fight against malicious software is the analysis of malware samples: Only if an analyst understands the behavior of a given sample, she can design appropriate countermeasures. Manual approaches are frequently used to analyze certain key algorithms, such as downloading of encoded updates, or generating new DNS domains for command and control purposes.
In this paper, we present a novel approach to automatically extract, from a given binary executable, the algorithm related to a certain activity of the sample. We isolate and extract these instructions and generate a so-called gadget, i.e., a stand-alone component that encapsulates a specific behavior. We make sure that a gadget can autonomously perform a specific task by including all relevant code and data into the gadget such that it can be executed in a self-contained fashion.
Gadgets are useful entities in analyzing malicious software: In particular, they are valuable for practitioners, as understanding a certain activity that is embedded in a binary sample (e.g., the update function) is still largely a manual and complex task. Our evaluation with several real-world samples demonstrates that our approach is versatile and useful in practice.

The full paper is available at http://www.iseclab.org/papers/ieee_sp10_inspector_gadget.pdf and will be presented in May at the 31st IEEE Symposium on Security & Privacy. The paper was joint work with Clemens Kolbitsch, Christopher Kruegel, and Engin Kirda - all members of the International Secure Systems Lab.

Waledac Infection Check

nospam@example.com (Thorsten Holz) — Tue, 02 Mar 2010 22:29:00 +0100

Ben Stock has implemented a web service to check a given IP address for infection with Waledac, similar to the Conficker Eye Chart. The idea is that we are currently tracking Waledac as part of the take-down effort and thus we have a pretty good overview of the individual bots within the botnet. Therefore we are in a position to determine if we have seen a given IP address in the recent past as a bot, which indicates that this IP address might be related to a Waledac infection. Of course, effects like NAT or DHCP need to be taken into account: if an IP address is not listed, this does not necessarily mean that you are not infected.

The check is available at http://mwanalysis.org/waledac/, feedback is welcome!

Waledac Takedown Successful

nospam@example.com (Thorsten Holz) — Thu, 25 Feb 2010 15:57:00 +0100

A few weeks ago, I blogged about our paper "Walowdac – Analysis of a Peer-to-Peer Botnet". The paper provides an overview of the Waledac botnet and its specific aspects compared to Storm Worm and similar peer-to-peer botnets. The paper also contains some measurement results for the botnet like the typical number of online bots and similar statistics.

In the last couple of days, the situation changed a bit: we worked on an active takedown of the botnet together with experts from Microsoft, Shadowserver, the University of Mannheim, University of Bonn, University of Washington, Symantec and others. The operation is know within Microsoft as "Operation b49" and involved domain takedowns and additional technical countermeasures. Microsoft also did some fantastic work on the legal side, the complaint filed by Microsoft ("Microsoft Corporation v. John Does 1-27, et. al.") is available online. As a result, the communication infrastructure of Waledac has been disrupted to a certain extent and the botmaster can effectively not send commands to the bots. The Waledac Tracker by sudosecure.net also shows a nice decline in the number of bots for the last few days. Note, however, that the infected machines are still up and running, thus some clean-up at that side is still necessary...

You can read more about the story in a blog post by Microsoft: "Cracking Down on Botnets". And I will update the blog with new information once we start to analyze the collected data...

Data Set For Malware Clustering/Classification

nospam@example.com (Thorsten Holz) — Fri, 29 Jan 2010 14:08:00 +0100

About one month ago I blogged about our research on malware clustering and classification. We have now also released the full data set from our experiments, such that other people can reproduce the results and compare our approach to theirs. You can find all information at http://pi1.informatik.uni-mannheim.de/malheur/, together with a description of the different data.

Quick overview of the data:

Our reference data set is extracted from our large database of malware binaries maintained at CWSandbox. The malware binaries have been collected over a period of three years from a variety of sources. From the overall database, we select binaries which have been assigned to a known class of malware by the majority of six independent anti-virus products. We append the overall anti-virus label to the filename of each report. Although anti-virus labels suffer from inconsistency, we expect the selection using different scanners to be reasonable consistent and accurate. To compensate for the skewed distribution of classes, we discard classes with less than 20 samples and restrict the maximum contribution of each class to 300 binaries. The selected malware binaries are then executed and monitored using CWSandbox, resulting in a total of 3.133 behavior reports in MIST format.

The application data set consists of seven chunks of malware binaries obtained from the anti-malware vendor Sunbelt Software. The binaries correspond to malware collected during seven consecutive days in August 2009 and originate from a variety of sources. Sunbelt Software uses these very samples to create and update signatures for their VIPRE anti-malware product as well as for their security data feed ThreatTrack. The complete test data set consists of 33.698 behavior reports in MIST format.

The full technical report is available at http://honeyblog.org/junkyard/paper/malheur-TR-2009.pdf.

Update: I changed the terms within the description to use the correct description.

Call for Papers: LEET'10

nospam@example.com (Thorsten Holz) — Mon, 25 Jan 2010 09:03:00 +0100

The submissions deadline for the 3rd USENIX Workshop on Large-Scale Exploits and Emergent Threats (LEET '10) is quickly approaching. Please submit your work by Thursday, February 25, 2010, 11:59 p.m. PST. The full call for papers is available at http://www.usenix.org/events/leet10/cfp/, see an overview below:

Topics
Now in its third year, LEET continues to provide a unique forum for the discussion of threats to the confidentiality of our data, the integrity of digital transactions, and the dependability of the technologies we increasingly rely on. We encourage submissions of papers that focus on the malicious activities themselves (e.g., reconnaissance, exploitation, privilege escalation, rootkit installation, attack), our responses as defenders (e.g., prevention, detection, and mitigation), or the social, political, and economic goals driving these malicious activities and the legal and ethical codes guiding our defensive responses.

Overview
Information technology (IT) adds $2 trillion annually to the US economy alone. While these technologies have enabled significant global economic growth, they have become rich targets for malicious activity. The US Federal Bureau of Investigation (FBI) indicated that cyber crime reached an all-time high in 2008; cyber crime now ranks as the FBI's third highest priority, behind such dramatic threats as counter-terrorism and counter-espionage. Much of this malicious activity is driven by economic incentives, but recently we have seen the emergence of highly visible, politically motivated attacks. While the motivations for malicious behavior and the technical mechanisms that enable them remain rich areas of research, it is clear that today our global society is faced with a wide range of cyber criminal activities: spam, phishing, denial of service, click fraud, etc.

Workshop Format
LEET aims to be a true workshop, with the twin goals of fostering the development of preliminary work and helping to unify the broad community of researchers and practitioners who focus on worms, bots, spam, spyware, phishing, DDoS, and the ever-increasing palette of large-scale Internet-based threats. Intriguing preliminary results and thought-provoking ideas will be strongly favored; papers will be selected for their potential to stimulate discussion in the workshop. Each author will have 15 minutes to present his or her work, followed by 15 minutes of discussion with the workshop participants.

"Studying Aspects of the Underground Economy"

nospam@example.com (Thorsten Holz) — Wed, 20 Jan 2010 06:53:00 +0100

Today I gave a talk at the International Computer Science Institute (ICSI) that focussed on some of the research I did in the past year. The slides are now available.

Abstract:
With the growing digital economy, it comes as no surprise that criminal activities in digital business have lead to a digital underground economy. Because it is such a fast-moving field, tracking and understanding this underground economy is difficult and most information in this area is vague. In this talk, we discuss several approaches to study the structure of these underground markets. In particular, we present a method with which it is possible to directly analyze the amount of data harvested through keylogger-based attacks in a highly automated fashion. Based on real-world data, we can get a glimpse into the digital underground economy. However, many open questions remain that will be discussed in the last part of the talk.

You can get the slides at http:///honeyblog.org/junkyard/presentations/10_underground-economy_ICSI.pdf.

Challenge 1 posted - Signed books as prizes!

nospam@example.com (Thorsten Holz) — Mon, 18 Jan 2010 08:56:00 +0100

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:

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

Walowdac – Analysis of a Peer-to-Peer Botnet

nospam@example.com (Thorsten Holz) — Sun, 03 Jan 2010 12:26:00 +0100

One of the most interesting botnets of 2009 was Waledac: the botnet implements a peer-to-peer-based communication channel and it can be seen as the successor of Storm Worm, since it implemented many similar ideas (e.g., a very similar language for spam templates was used). The researchers from Trend Micro had published an analysis of the botnet and we also examined the botnet. The result is a paper entitled "Walowdac - Analysis of a Peer-to-Peer Botnet": instead of passively observing the network, we implemented an active infiltration component. We emulate the protocol of a bot and are able to observe the inner communication aspects of the network. As a result, we obtain an in-depth overview of the botnet that enables us to study different aspects of the network, e.g., efficiency of the spam campaigns or number of active bots. As a small peak of the results, the following pictures shows the number of active bots in different countries on a specific day in August 2009. We can for example observe diurnal patterns and clearly see the effects of timezones on the size of the botnet:

Abstract:
A botnet is a network of compromised machines under the control of an attacker. Botnets are the driving force behind several misuses on the Internet, for example spam mails or automated identity theft. In this paper, we study the most prevalent peer-to-peer botnet in 2009: Waledac. We present our infiltration of the Waledac botnet, which can be seen as the successor of the Storm Worm botnet. To achieve this we implemented a clone of the Waledac bot named Walowdac. It implements the communication features of Waledac but does not cause any harm, i.e., no spam emails are sent and no other commands are executed. With the help of this tool we observed a minimum daily population of 55,000 Waledac bots and a total of roughly 390,000 infected machines throughout the world. Furthermore, we gathered internal information about the success rates of spam campaigns and newly introduced features like the theft of credentials from victim machines.

The paper was joint work with Ben Stock, Jan Göbel, Markus Engelberth, and Felix C. Freiling. The full paper is available at http://honeyblog.org/junkyard/paper/waledac-ec2nd09.pdf and it was published at EC2ND 2009.

Automatic Analysis of Malware Behavior using Machine Learning

nospam@example.com (Thorsten Holz) — Mon, 28 Dec 2009 12:15:00 +0100

In the last couple of years, several honeypot solutions to automatically "collect" malware samples were developed. With these tools, it is possible to obtain copies of malware samples without any human interaction. As a result, we are able to collect quite a few malware samples per day, which then also need to be analyzed. Thus, several sandbox solutions were developed that automate the analysis step by performing dynamic, behavior-based analysis. The result of the dynamic analysis is typically a report that summarizes the observed behavior. The next logical step is to use that information to perform malware classification and malware clustering: at the end of that process, we can then obtain information about which samples perform basically the same kind of activity. We can then automatically find variants of well-known threats, identify new malware families, and reduce the manual effort needed to analyze the large number of incoming malware samples.

In the last couple of months, we worked on malware classification and malware clustering. The results are summarized in a technical report. In the article, we introduce a learning-based framework for automatic analysis of malware behavior. To apply this framework in practice, it suffices to collect a large number of malware samples and monitor their behavior using a sandbox environment. By embedding the observed behavior in a vector space, reflecting behavioral patterns in its dimensions, we are able to apply learning algorithms, such as clustering and classification, for analysis of malware behavior. Both techniques are important for an automated processing of malware samples and we show in several experiments that our techniques significantly improve previous work in this area. For example, the concept of prototypes allows for efficient clustering and classification, while also enabling a security researcher to focus manual analysis on prototypes instead of all malware samples. Moreover, we introduce a technique to perform behavior-based analysis in an incremental way that avoids run-time and memory overhead inherent to previous approaches.

Abstract
Malicious software — so called malware — poses a major threat to the security of computer systems. The amount and diversity of its variants render classic security defenses ineffective, such that millions of hosts in the Internet are infected with malware in form of computer viruses, Internet worms and Trojan horses. While obfuscation and polymorphism employed by malware largely impede detection at file level, the dynamic analysis of malware binaries during run-time provides an instrument for characterizing and defending against the threat of malicious software.
In this article, we propose a framework for automatic analysis of malware behavior using machine learning. The framework allows for automatically identifying novel classes of malware with similar behavior (clustering) and assigning unknown malware to these discovered classes (classification). Based on both, clustering and classification, we propose an incremental approach for behavior-based analysis, capable to process the behavior of thousands of malware binaries on a daily basis. The incremental analysis significantly reduces the run-time overhead of current analysis methods, while providing an accurate discovery and discrimination of novel malware variants.

The full technical report is available at http://honeyblog.org/junkyard/paper/malheur-TR-2009.pd. It was joint work with Konrad Rieck, Philipp Trinius, and Carsten Willems. And the word cloud was generated using http://www.wordle.net/.

AV Tracker

nospam@example.com (Thorsten Holz) — Thu, 22 Oct 2009 12:49:00 +0200

A couple of days ago, the website "AV Tracker" went online, which publishes information about various automated analysis systems. The idea is that the attacker uploads a binary to an analysis system, waits for the sample to be executed, and then the binary phones home some information to a server under the control of the attacker. The collected information is then published at "AV Tracker", exposing information about the analysis systems. Besides some well-known AV companies, also CWSandbox and Anubis were affected.

We analyzed the binary and found that it sends a simply HTTP request, in which all extracted information is encoded. An example for an analysis report generated by one of the samples is http://anubis.iseclab.org/?action=result&task_id=361b5a8ee7235954252b02d33b3a7d24. This can be defeated by blocking access to the reporting server or by regularly changing the IP address of the analysis systems, but at the end this will be some kind of arms race again.

Some other interesting information is also embedded in the binary. When extracting the strings from the sample, the following text becomes visible (some information is hidden by dots):

This is Peter Kl....... fuck ...... fuck the world fuck you all!
I was once working with ...... and was a white hat, now I am the worst mean motherfucker black hat and I am selling the source code of ...... .. :D
I am with the ~~Sinowal~~Whistler developers, funny days, aren't ;) and fuck ..... they don't have no idea :D bitches

A related article was also published today at http://www.viruslist.com/en/weblog under the title "A black hat loses control".

$645.00 ...

nospam@example.com (Thorsten Holz) — Thu, 10 Sep 2009 23:27:13 +0200

... is the amount I am worth in the underground economy, at least according to Symantec's new website on which they advertise (in a somewhat entertaining way) Norton 2010 products. Here are the results when I take the risk assessment:

[...] In the underground economy, you're really worth about $645.00. And that's on a good day.
Your entire digital life could go on the auction block for as little as $10.96, whether you like it or not.

How they compute these numbers and on what methodology / measurements this is based remains completely unclear, after all it is just some kind of marketing. But the movies are funny, perhaps they can serve as some kind of security awareness campaign. Main drawback is that the website is almost completely built on top of Flash and JavaScript - how about not using all these techniques next time? In some recent measurements we found that the vast majority of web surfers still have an unpatched version of Flash installed, better teach them to regularly update their system next time...

Thread Graphs for Visualizing Malware Behavior

nospam@example.com (Thorsten Holz) — Tue, 25 Aug 2009 23:33:00 +0200

The last blog post dealt with our recent research on visualizing malware behavior. Now a quick update on the thread graphs we generate for visualizing malware behavior: since tree maps display nothing about the sequence of operations, we use another presentation format to visualize the temporal behavior of the individual threads of a sample. A thread graph can be regarded as a behavioral ﬁngerprint of the sample that represents the temporal order of executed system commands and the different threads spawned by a binary. The x-axis represents the time (sequence of performed actions), while the y-axis indicates the operation/section of the performed action. An analyst can then study this behavior graph to quickly learn more about the actions of each individual thread.

The following two pictures show examples of this kind of visualization:

On the left hand picture, we can see that one thread is responsible for the majority of operations for the sample. This thread performs many registry operations and initially performs many network- and system-related operations (operations 90-140). Additionally, two more threads are spawned, but they perform only a limited amount of operations during the analysis phase. The thread graph for the malware sample on the right side is completely different and an analyst can get a quick overview of what actions a given samples performs.