[This is a Guest Diary by Jeffray Spence, an ISC intern as part of the SANS.edu BACS program]
Around Here, we start our day with coffee, but our day does not begin until we have our data…So much data. Where to even begin?
When approaching a massive dataset, such as those from a DShield SIEM Honeypot, the first step is to actually find something to analyze. As for myself, I usually go looking for something that makes me go; "Huh."
Today we will be walking through a starting approach with Kibana Discover to search out embedded language preferences in webtraffic, then we will use this as a starting point for finding anomalous activity.
Find A Problem to Get Some Data
Inside of my DShield SIEM there are nearly a million records, so I approach by looking for a slightly smaller data carve with which to work which still represents the problem set:
Anyone with Elastic Kibana can perform this approach on their webtraffic data as long as the HTTP headers are provided. Here, the logs are from my SANS Dshield Honeypot and the accompanying Dshield SIEM. For those who desire to install this setup there are plenty of great resources out there with links at the end of this article [1] [2].
In slightly more than 3 weeks of observation the rasperry pi honeypot had transferred a Million Records into the Elastic SIEM. These many records can prove very challenging when figuring out where to start, so it is helpful to know that even with no filtering applied on the Discover page the Field Statistics will give some fairly detailed metrics about what fields exist in the current Index.
On the Discover page in the upper right, I selected my index (here the index is “cowrie”) and without any filter applied I selected Field Statistics:
Calculating statistics ourselves is possible using ESQL and Stats, however this feature of Elastic makes gaining visibility much more obtainable at the onset. We are immediately presented with a screen displaying all of the Documents broken down by Top Values which potentially gives us some valuable insight:
The resultant statistics show that in these 3 weeks my honeypot had generated 960,000 logs, but all of those are only within 4 rules. Four is less than a 960,000 so this gives a more manageable starting dataset:
“Huh.” This little raspberry PI has been humming away quietly all day and all night and only 4 rules have popped up. I found this interesting. With no filtering applied only 15,271 records triggered any rule?
This setup is a honeypot, so literally every record is suspicious. So, what about the other 944,866 records which triggered no rules?
Today, we want a problem not a project, so we are going to use one of these rules to narrow down our traffic for analysis.
We have rule triggers for the Index page. Boring. Great for generating a large dataset, but this is not a very targeted approach.
Git Config is something I had previously analyzed, and this led me to discover malicious traffic attempting to scrape Git Config from internet hosts. Neat, but not for today.
Fortinet Emulation, for those who don't know, has fairly recently gotten a lot of attention [3].
Only one detection so far in my honeypot, so for me that's just an outlier. Interesting, but not much of a corpus.
Through the process of elimination, we are left with the Sonicwall Appliance Login Page rule.
Good representation over the problem space with 41 individual hosts logging in activity. This could potentially demonstrate how an attack is conducted or give a clue as to who was conducting one. Perfect because we are looking for regular and repeating attack attempts. I selected these criteria as a Kibana filter.
Now to start digging.
Data Selection
Now that we have a smaller dataset to work within, we need to decide what we want to find. After that we need to figure out how to actually find that.
This first approach is what I would NOT recommend One do:
Noticing here that every field statistic for all 157 records has One Distinct Value and only1 category under Distribution tells us these fields are ALL common to ALL records. A field common to every record will have a unique value for each record.
We don't want common, we want weird: the approach I will use is to flip it and look at Distribution 2 or higher. This is Long tail analysis:
Here we see the Distinct values are now descending with the “sweet spot” between 2 and 6 so. Not an exact criterion, but one that is practical for manual review.
Within these fields we have fields of interest:
When fields contain similar values, they may illustrate a relationship between the data represented. Here the traffic triggering the Sonicwall Rule contains a relatively small subset of unique values. The next step is to find something we cannot readily explain away; a simple exercise to conduct on the “tags”, “host.ip”, and “host.mac” fields. The IP and MAC are going to be both interfaces of my Dshield Pi itself, while the Tags are going to be metadata about how the rules generate. The “source.geo.continent_code” is not very useful in a Virtual Private Server (VPS) enabled world but may prove useful later. This leaves 2 fields:
http.request.connection
"Close" and "close". Most traffic parsing tools are going to convert these all to lowercase string, and thus never see this small difference.
. No longer interesting.
http.request.mime_type
"utf-8" (the defacto choice) verses some interesting mime encoding…
Next was to break down what this mime encoding is saying: "GBK,utf-8;q=0.7,*;q=0.3"
This string is sent to a webserver as the Accept-Charset HTTP header. Values of GBK,utf-8, and wildcard (*) are present. The preference weights are specified as "q" (for “quality value” by convention). Since the implicit maximum weight value is q=1.0, the simplified character set preferences are:
(GBK;q=1.0),(utf-8;q=0.7),(*;q=0.3)
I think of this as a preference for what character encoding to use:
(Send me “GBK” 100% of the time, if not “utf-8” 70%, and if not Whatever 30%)
UTF-8 makes sense as does wildcard (*). However, GBK is explicitly the extension of the “GB 2312” character set for Simplified Chinese characters.
"Huh."
Why do these requests explicitly state they greatly desire to transfer data to my Honeypot, which is not in China, using an exclusively Chinese character set?
I also apply this as a filter in Kibana to narrow our results to 8 Records:
Two issues are now present. This dataset of 8 records is too narrow to work with, and also, we don’t actually care about the Sonicwall rule anymore now that we find some interesting traffic. Next, we need to pull in some additional data for this mime type field.
Finding commonality
So my Elastic SIEM, the webhoneypot index is for Webserver Logs, and my cowrie index shares these fields. Here the filter works for both.
At this point we are not interested in the “Sonicwall Appliance” filter anymore, so we remove it. We are now using the “Mime Type” field to work out the language preferences and have 47 interesting records within the http.request.mime_type:
These records now represent every inbound connection attempt sending this exact same format string for the GBK Character set preferences. There are 47 requests in total which send the identical preference header. How can we use this header to find odd traffic in our data?
The problem set has not changed, only our dataset. We repeat the same process of sorting the Distribution Field under the Field statistics section.
Following this Distribution Analysis process we see two User-Agents, which is interesting.
However, we also very clearly see there are two source.geo.region_name values:
“Beijing”, yeah, ok that makes sense. Beijing is in China… but “Ontario”?
"Huh". People do move around, however, following a purely statistical analysis approach one would not anticipate that Canadian Cyber actors would greatly desire a Chinese Character set while targeting my American Honeypot. This presents as an anomaly.
We add the source.geo.region_name: Ontario filter and observe there are now only 3 records:
At this point I exported these three records to Libre Office so I could look at them:
Modern VPS availability makes the Autonomous System (AS) number far more useful than an IP: AS-7456 is the system responsible for all three of these records.
This field, “source.geo.as.number” will be our next field to analyze:
What’s Your Number?
We now set our new filter to be source.geo.as.number: 7456
We remove all other filters and follow the same “Distribution Analysis” as before. We immediately find the same pattern:
There is again a language preference but this time in a different field:
The http.request.accept-language encoding for all 31 requests from AS-7456 read as either:
zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.628 at (90.3%)
OR
zh-CN,zh;q=0.83 at (9.7%)
"zh-CN" means "Mainland Chinese" Language preference.
"zh" just means any version of Chinese.
For all 121 records observed for this AS there were 31 alternative language preference specified.
This is an optional header, however the indication of a desired alternate language preference can narrow down our search scope considerably.
I applied the filter as:
http.request.accept-language: "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6" OR http.request.accept-language: "zh-CN,zh;q=0.8"
At this point I exported the 31 records for this filter:
I took these records over to LibreOffice as a Tab Separated Value sheet so I could have a look at it.
When I did the pattern was unmistakable:
These are all regularly scanning internet hosts for the same set of endpoints all from the same Canadian based AS-7456:
165[.]154.36.91
165[.]154.29.93
165[.]154.11.140
165[.]154.120.226
165[.]154.33.72
165[.]154.182.221
Values for http.request.body.content were all consistent repetitions of:
/cgi-bin/login
/config.json
/sitemap.xml
/robots.txt
/favicon.ico
/
There is no attempt being made to conceal the fact that these user agents strongly desire Chinese character sets in the HTTP Language preferences while actively targeting internet facing endpoints. We have discovered an obviously automated campaign.
Interestingly, tzrhxln[.]cn (165[.]154.29.93) is a suspect high entropy domain.
Similar domains also appear in this dataset as extracted with this ESQL Query:
source.domain values:
leheedu[.]cn
mail.,brossy.com[.]cn
mail.brossy.com[.]cn
tzrhxln[.]cn
All of these domains operate from AS-7456, have strong CN presence, and are actively scanning.
One Conclusion
This demonstration of a simple approach to Elastic Discovery using optional HTTP language preferences and mime types is only an example. We have found high entropy domains, bot scanners, and plenty of nefarious intent, however the most interesting aspect in my view is the approach we have taken. Expanding upon this could yield automation.
Typically, when analyzing datasets in a SIEM it is common to look for all the fields that match a correlation. In this approach we attempt to match the least commonality required to gain a single insight into what hosts may be related and then repeat the data acquisition process with this selected feature. This is featuring selection, and because we are purposely iterating upon the selection with intent, we are functionally engaged in rudimentary feature engineering.
Using the embedded language preferences in webtraffic as a starting point we were able to trim a massive corpus into a manageable selection. We have also built upon some understanding of how these language headers appear in logs when browsing, so we could always simply begin selecting those features as a starting point in the future when processing SIEM data.
Just a simple example for today, but we eventually found what we were looking for. Also, most importantly, the more efficient we are in the search, then the more time we have for our coffee.
[1] https://github.com/dshield-isc/dshield
[2] https://github.com/bruneaug/DShield-SIEM
[3] https://www.fortinet.com/blog/psirt-blogs/analysis-of-reported-credential-compromise-of-fortigate-devices
[4] https://www.sans.edu/cyber-security-programs/bachelors-degree/
———–
Guy Bruneau IPSS Inc.
My GitHub Page
Twitter: GuyBruneau
gbruneau at isc dot sans dot edu
(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.