The Magic of DGA Discovery

There are essentially two types of magic in the world – those that deceive an audience in to believing something impossible has occurred, and that which is best defined by Arthur C. Clarke as “any sufficiently advanced technology is indistinguishable from magic”.

The antivirus industry is rife with the former – there’s no shortage of smoke and mirror explanations when it comes to double-talking “dynamically generated signature signature-less engines” and “zero false-positive anomaly detection systems”.

I’m going to introduce you to the later kind of magic. Technological approaches so different from traditional approaches that, for many folks out there in Internet-land, it’s indistinguishable from magic. More than that though, I’m going to try to explain how such techniques are reversing the way in which threat discovery has occurred in the past. However what I’m not going to do is to even try to explain a fraction of the math and analytics that lies behind that magic – at least not in a blog!

Oh where, oh where should we start?

Let’s begin, for arguments sake, by classifying malware as a tool; a weapon to be more precise. In the physical world it would be easy to associate “malware” with the bullets from a gun, and the gun in turn likened to perhaps a drive-by download site or a phishing email. In response to that particular physical threat, there are a number of technological approaches that have been deployed in order to counter the threat – we have metal detectors and x-ray machines to alert us to the presence of guns, sniffing technologies to identify the presence of explosive materials, CCTV and behavioral analysis systems to identify the suspects who may be hiding the gun.

A fundamental premise of this layered detection approach is that we’ve encountered the threat in the past and already classified it as bad – i.e. as a “weapon”. Gun equals bad, knife equals bad, metal corkscrew equals bad, and so on. Meanwhile everything else is assumed to be good – like an ostrich egg – until it happens to be used as a weapon (such as when “Russell pleaded guilty to assault using an ostrich egg as a weapon, assault, and breaching a protection order“) and inevitably some new detection technique is proposed to detect it.

Traditionally the focus has been on “preventing” the threat. In particular, detecting the presence of a known threat and stopping it from reaching its target. In the physical world, in general, the detection technologies are pretty robust – however (and it’s a big “however”), the assumption is that the technology needed to provide this prevention capability is ubiquitous, deployed everywhere it could potentially be needed, and that it works every time. Sure, at high value targets (such as airports) you’ll find such technology employing its optimal capability, elsewhere though (such as the doorway into your home) it’ll not be encountered. There are obvious parallels with the cyber-world here – except arguably the Internet-equivalent technologies are a little more ubiquitous, but considerably less capable in preventing the malware threat.

For the mob hitman, serial killer, or other kind of mass murderer, the threat of sparsely deployed metal detectors is an easily avoided problem. Subversion or avoidance of the detection systems is pretty easy and, more importantly, an appropriate choice of location negates the problem entirely. Even then, such a detection strategy, operated in isolation, isn’t a serious inhibitor for new murders. If such a technology exists to only detect the guns and bullets, but is not capable of providing attribution (e.g. this gun was used in 16 different murders over the last 2 weeks and the owner of this gun is the murderer), then the criminal only ever loses a tool each time they get caught – since the prevention technology is divorced from association with the victims (past or prospective).

But there’s an entirely different side to dealing with that kind of threat – and that’s the forensics element. While our not-so-friendly murderer can avoid the detection technologies, they’re much less capable of avoiding the evidence of violent past actions. Starting from the first murder, it is possible to build a case that points to a specific criminal by analyzing the components of the crime scene(s).

I know, the argument is that everything should be done to prevent the crime in the first place. That’s clearly very difficult in the physical world, but you’re basically living in a fantasy land of Goblins and Unicorns if you’re expecting it to work better in the cyber-world.

Which basically brings me to the discussion (and subsequent detection) of the latest generation of sophisticated malware – malware that uses domain generation algorithms (DGA) to locate their command and control infrastructure and upload their stolen data. Malware with this capability are designed to evade all those conventional prevention technologies and, once successfully deployed within their victim populace, evade all other methods of traffic filtering and takedown processes. Even if a malware sample is accidentally captured, its DGA capabilities will go undetected.

Detecting DGA-based malware is, as I implied earlier, both “magic” and a reversal of conventional prevention approaches. In order to detect DGA-based threats early on, you start with the victims first…

DGA-based malware use an algorithm to pick out candidate domain names in order to hunt for their prospective C&C servers. The vast majority of the domain names they’re looking for simply don’t exist. In the world of DNS, attempting to resolve a domain name that doesn’t exist will result in a “no such domain” (i.e. an NX) response from an authoritative DNS server somewhere down the line. So, in essence, DGA’s are noisy if you’re watching DNS activity – and lots of NX responses are a key feature of an infected host. Unfortunately, the average Internet-connected device typically tries to look up lots of things that don’t exist, and there’s often a lot of legitimate NX traffic which can disguise the flapping of the malware.

Assuming some kind of algorithmic basis to the domain candidates being created by the malware, you could suppose that it would be possible to develop a unique signature for them. If only it was that easy – the criminals are smarter than that. And you’re also assuming that you’ve already encountered a copy of the malware before in order to create a signature for that particular DGA-based malware.

Instead there’s a much better way – you monitor all your DNS traffic and NX responses, and you identify clusters of devices (or IP addresses) that are generating roughly similar domain name requests. This first pass will provide a hint that those devices share some kind of common ailment (without ever needing to know what the malware is or have ever encountered a sample before). In a second pass you focus upon identifying just the domain names that are structurally very similar across all the afflicted assets and classify that DGA based upon a number of features. Classifying the DGA makes it easier for attribution and uniquely tracking a new threat.

At this point you’re pretty much sure that you have a new malware outbreak and you know who all the victims are, and you can easily track the growth (or demise) of the botnet within your network. Unfortunately you’re also dealing with a brand new threat and you probably don’t have a malware sample… and that’s where an additional layer of analytics comes into play and more “magic” happens as you automatically begin to identify the domain names that are tried by the DGA-based malware that actually succeed and engage with the criminals server.

Let’s work through a simple example uncovered last week by Damballa Labs. Over the weekend one of our NX monitoring tools identified a few thousand IP addresses within our customer base that were generating clusters of NX DNS traffic very similar to the following:,,,,,,,,,,,, ,,,,,,,,,,,,,,

While you’d be hard pressed to write a “signature” for the domain names that wouldn’t cause false positives out the wazoo, the features of the combined victim’s traffic (frequency, distribution, commonality, etc.) work fine as a way of associating them to a shared new threat.

Armed with this knowledge, it is then possible to identify similarly structured domain names that were successfully resolved by the victims that also shared timing elements or appeared to alter the flow of NX domain responses. For example, if the DGA-based malware is designed to locate a “live” C&C server, once it’s found a server it probably doesn’t need to keep on looking for more and will likely stop generating NX domain traffic for a period of time.

Based upon our observations of this particular botnet outbreak, it was possible to identify the following C&C servers being operated by the criminals:

  • ###.133. ###.247 –
  • ###.133. ###.247 –
  • ###.133. ###.247 –
  • ###.133. ###.247 –
  • ###.133. ###.247 –
  • ###.133. ###.75 –
  • ###.133. ###.75 –
  • ###.133. ###.191 –
  • ###.133. ###.191 –
  • ###.133. ###.191 –
  • ###.133. ###.191 –
  • ###.133. ###.191 –
  • ###.133. ###.191 –

[NOTE: We've temporarily obfuscated some of this data while we continue to investigate and enumerate the global pool of victims. We'll release the technical details of this particular DGA-based botnet soon…]

So, by this point we know who the victims are, how many C&C servers the criminals are operating, what their IP addresses are and, subsequently, which hosting facilities they are operating from:

  • AS13237 LAMBDANET-AS Lambdanet Communications Deutschland GmbH


What about the malware piece? While we know who the victims are, it would be nice to know more about the tool that the criminals behind this botnet prefer and ideally to get to know them more “personally” – if you know what I mean…

As it happens, there are some malware samples that have been discovered in the very recent past that also like to contact C&C’s running upon the same hosts at this facility. For example:

  • d977ebff137fa97424740554595b9###

Fortunately, while the malware sample wasn’t detected by any antivirus products, it had previously been automatically executed within our dynamic analysis environment and we’d already extracted all of its observable network features, including an additional (successful) C&C engagement:

  • using ###.133. ###.75, ###.133. ###.191, and ###.23. ###.139

This, in turn, helped identify the following additional hosting facility based in the Netherlands:

  • AS49981 WORLDSTREAM WorldStream

There’s obviously much more to this particular threat, and if you’d like to get involved digging into it and helping with the attribution please let me know…

But, getting back on track, this approach in identifying brand new threats – while sounding like magic to many – works really well! We’ve found it to be immensely scalable and fantastically accurate.

However, there’s one fly in the ointment (as it were)… the approach identifies new threats long before the malware component is typically uncovered by the security community and is independent of the the vector used to infect the victims,the malware tool that was deployed and ultimately the actual endpoint device type. This same detection capability is what enabled us to detect the Mac Flashback malware in our customer environments weeks before the malware was ‘discovered’.

Think of it this way. Based upon the forensics of the blood splattered bodies and evidence left in the room that the murderer left behind, we know that the victims were bludgeoned to death with an ovoid object approximately 6 inches in diameter, weighing 3 pounds and composed of a calcium shell. We also know that the murderer was 5 foot 11 inches, weighed 240 pounds and wears size 11 Reeboks.

You can keep your metal detectors for all the good that’ll do in this case…

– Gunter Ollmann, VP Research

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