Archive for the 'Monitoring & Reporting' Category

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How Big is Big? Some Botnet Statistics

There is a lot of malware out there, and sometimes it’s very difficult for security researchers or AV-vendors to estimate the extent of such a threat (eg. a trojan). One technique to do is called sinkholing: The goal is to register malicious botnet domains proactively or reactively to prevent the criminals exerting command and control over hijacked/infected computers, and at the same time warn ISPs of infected computers.

Some of you might already know that I am running a sinkhole. Therefore I thought it might be interesting to reveal some botnet Statistic based on the drone data I have collected on my sinkhole.

The following data has been collected over a period of 2 months. During this time I’ve sinkholed several botnets. To generate the statistics shown below I have picked out the highest peak of each malware family and printed it to the bar chart. In short this means that the chart shows the highest peak of each malware family during the past two months (within a 24 hour period).

First of all, let’s have a look at each malware family I’ve sinkholed during this time.

Trojan Aliases Reference
Artro Renos, CodecPack Kaspersky Lab
Carberp - Symantec
Gbot - Sonicwall
Gozi - SecureWorks
Ponmocup Swisyn, Changeup Microsoft
Ramnit - abuse.ch
SpyEye EyeStye Symantec
TDSS Alureon, Tidsserv, TDL4 ESET
ZeuS Zbot, WSNPoem, ntos Symantec

As shown in the table above we have some banking trojans (Carberp, Gozi, SpyEye and ZeuS), some trojan droppers (Gbot, Ponmocup), a worm (Ramnit) and some Click fraud trojans (Artro, TDSS).

Note: The numbers of infected IPs for each trojan mentioned below does not necessarily reflect the exact botnet size. It does however work fairly well as a relative indication. Some trojans are malware kits being used to run several different botnets (Like ZeuS or SpyEye), where all are not being sinkholed.

Let’s take a look at the sinkhole statistics:

The chart above shows the total number of new and total IPs seen within 24hrs for each malware family. What really sticks out is the fact that the trojans that are being used to attack financial institutions (banking trojans) has a relatively small amount of infected computer (drones) compared to Gbot (that is used to drop/install additional malware on the victims computer) and the well-known click fraud rootkit called TDSS. The size of the TDSS botnet is 6 times the size of the Carberp botnet.

Why is this the case? It’s not very difficult to infect computers today. The trick is to find a good way to monetize the botnet. For banking trojans, the problem becomes getting money mules that the criminal can use for transferring/laundering the stolen money. A cybercriminal won’t benefit from a big botnet if he’s not able to cash out the money from the bank accounts of the victims. Also, banking trojans rather quickly gets attention from both Law Enforcement and individuals in the infosec community.

Doing click fraud is much easier: Who cares about click fraud? Nobody, except the companies that are actually offering/selling online advertisement. If you call someone and tell him “Hey, your computer is infected with a click fraud trojan” you will most probably get a answer like “WTF is click fraud?!?” and even if you explain the situation to him I’m pretty sure you will get an answer like “Well I don’t care, I hate online advertisements anyway. They only distract me when I’m surfing on porn sites… *erm* when I’m doing online shopping”.

Still, I’m not surprised that there are botnets out there that are even bigger than TDSS/TDL:

The chart above shows a botnet that is called Artro. It is also known as “The advertisement botnet” (Kaspersky) or Renos/CodecPack. It is 1,5 times bigger than TDSS. However, Artro is also doing some click fraud stuff. I sinkholed the Artro botnet a year ago. Back then, the botnet had a size of 330’000 infected computers (of course within 24hrs)!

So I’m asking myself: Does this answer our question “How Big is Big”? If we are serious we can say that 330’000 infected computers is quite enough and really big. That’s nearly the same amount of computers as there are inhabitants in the largest Swiss city (Zurich).

What would you say if I told you that there is a botnet out there that is much bigger than the Artro botnet?

Some weeks ago I came across a huge botnet that was pretty unknown to me and that I never had heard of before. Doing some research I came to the conclusion that this trojan was known as Ponmocup. When I’ve started to sinkhole this botnet I was shocked as I saw that more than 1,2 million (yes, 1’200’000) unique IPs connected to my sinkhole just within 24 hours..

Probably most of you don’t even know Ponmocup, so you may ask yourself how this botnet became that big. Well you already answered this question: The criminal obviously managed to stay under the radar for months (maybe even years). I’m sure there are even more botnets out there (like Artro and Ponmocup) that are quite big and still under the radar of the AV-industry / infosec community.

*** Conclusion ***
We have learned that the botnet sizes doesn’t really matter. The criminals don’t need to have a big botnet to make a lot of money: It always depends on the business model the criminals wants to adopt (doing ebanking fraud, clickfraud or whatever).

But what do we have to do to mitigate these threats? My approach is to try to identify such botnets and sinkhole them. Doing so I’m able to collecting data from the connecting bots, which are being fed into the Shadowserver Drone database. If you are an ISP, a company or running your own network/AS you can obtain free-of-charge Drone feed from Shadowserver for your AS. This allows you to get informed about infected computers within your network on a daily basis.

If you are an ISP/network owner I highly recommend you to subscribe to Shadowservers Drone feed (if you are not already subscribed).

You can subscribe and/or obtain more information about Shadowserver’s Reporting Service here:
http://www.shadowserver.org/wiki/pmwiki.php/Involve/GetReportsOnYourNetwork

Follow me on Twitter:
twitter.com/abuse_ch

*** Further links ***

Introducing: Palevo Tracker

Today we are going to talk about a nasty worm called Palevo.

Palevo (also known as Rimecud, Butterfly bot or Pilleuz) made some big press in 2009 when Panda Security announced the coordinated takedown of a huge botnet that they called Mariposa.

Since then the threat lost its media attention, but what most people don’t known is: Palevo is still a big player in the global threat landscape. According to FireEye, in 2010 Palevo was the top malware (# of infections) in the world:


Source: FireEye’s Malware Intelligence Lab: World’s Top Malware

Palevo is a so called bot kit that is being sold in underground forums (like ZeuS) using the name BUtterFly BOT. Therefore there are dozens of different botnets out there run by different criminal groups.

So what is the key to the success of Palevo? The worm is using different techniques to spread itself. The most common builtin techniques include:

  • P2P filesharing programs (bearshare, imesh, emule, limewire etc.)
  • Instant messaging (MSN- / Windows Live Messenger)
  • Removable drives (like USB-Sticks)

In addition, criminals have been observed linking other spreading mechanisms such as windows filesharing spread with palevo to achieve maximum impact.

During the past few months I have come across dozens of USB sticks infected with a variant of Palevo. Unfortunately, most (new) Palevo samples have a very bad detection rate. This makes it pretty easy to get infected. Just imagine you are attending a meeting or event, and you ask your colleague or the presenter to get a copy of the presentation he just held a few minutes before. What will he do? Well, most probably he will provide you with his USB stick with a copy of the presentation and BOOM – you are infected.

Another aspect of the problem is the fact that most employees are using the same USB stick at home and at work. If they plug-in the USB stick (which were previously infected by Palevo on the home computer) into the office computer, Palevo will infected it immediately. In this case it doesn’t matter what corporate Firewall or what Spam-Filter you are using in your network – you will get infected before most of the corporate security devices have had a chance to kick in.

In spite of Microsofts decision to disable autoplay in Windows 7, and the highly needed disabling of autorun (except for CDs) in XP/Vista/2003/2008, Palevo still seems to spread widely.

A further problem is the way Palevo communicates with its Command&Control server (C&C): The worm uses UDP and encrypts the data sent to the C&C server on (in most cases) a high port (e.g. 7700 UDP). The reason why Palevo uses UDP is simple: There is a bunch of Firewalls/Appliances out there which are poorly configured and therefore:

  • aren’t logging UDP packets in the Firewall log
  • allow UDP traffic by default

That makes it pretty easy to keep the Palevo C&C traffic hidden even in corporate networks.

*** Palevo Tracker ***
As outlined above, Palevo is a huge threat for corporate- and home networks. Due to the fact that it is spread widely and most people are not aware of the problem I have decided to create Palevo Tracker. My goals are:

  • Get some attention on the Palevo threat
  • Provide a blocklist for well known Palevo C&Cs to the internet community
  • Provide details regarding Palevo C&Cs to ISPs, CERTs and Law Enforcement
  • Keep the project smart and simple as possible

To keep it simple I’ve created Palevo Tracker as sub-project on AMaDa. This means that the Palevo Tracker blocklist is included in the AMaDa C&C Blocklist.

You can use the blocklist to block Palevo C&C traffic proactively and/or to identify infected clients (e.g. by matching the blocklist against your Firewall logs).

*** Further Links ***
Below are some links to different AV-vendors currently detecting Palevo:

Symantec: W32.Pilleuz
McAfee: W32/Palevo
Microsoft: Win32/Rimecud
Symantec Connect: The Mariposa Butterfly

Follow me on Twitter: twitter.com/abuse_ch




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