Tuesday, July 7, 2020

Insider Threat Hunting

If you subscribe to the notion that a user, who is intent on stealing data from your org, will require a change in their behavior.  Then identifying that change is critically important.  As this change happens, they will take actions that they have not previously taken.  These actions can be seen as anomalies and is where we want to identify and analyze their behavior.

I've been studying insider IP theft, particularly those with a connection to China, for a number of years now.  I feel that, in a way, this problem mimics the APT of 10 years ago.  Nobody with exposure to it is willing to talk about the things that work or don't.  This leaves open the opportunity for this behavior to successfully continue.  While I'm not going to share specific signatures, I would like to talk about the logic I use for hunting.   This is my attempt to generate conversation around an area that, in my opinion, can't be ignored.

Just as in network intrusions, there are phases that an insider will likely go through before data exfil occurs.  But unlike network intrusions, these phases are not all required as the insider probably has all the access they need.   They may perform additional actions to hide what they are doing.  They may collect data from different areas of the network.  They may even  simply exfil data with no other steps.  There are no set TTP's for classes of insiders, but below are some phases that you could see:

Data Discovery
Data Collection
Data Packaging
Data Obfuscation
Data Exfil

I've also added a few additional points that may be of interest.  These aren't necessarily phases, but may add to the story behind their behavior.  I'm including it in the phase category for scoring purposes though.  The scoring will be explained more below.  The additional points of interest are:

Motive - Is there a reason behind their actions?
Job Position - Does their position give them access to sensitive data?
Red Flags - e.g. Employee has submitted 2 week notice.

By assigning these tags, behaviors that enter multiple phases suddenly become more interesting.  In many cases, multiple phases such as data packaging -> data exfil should rise above a single phase such as data collection.  This is because a rule is only designed to accurately identify an action, regardless of intent.  But by looking at the sum of these actions we can begin to surface behaviors.  This is not to say that the total count of a single rule or a single instance of a highly critical rule will not draw the same attention.  It should, and that's where rule weighting comes in. 

Weighting is simply assigning a number score to the criticality of an action.  If a user performs an action that is being watched, a score is assigned to their total weight (weighted) for the day.  Depending on a user's behavior, their weighted score may rise over the course of that day.  If a user begins exhibiting anomalous behavior and a threshold is met, based on certain conditions, an alert may fire.

An explanation of alert generation.  My first attempt at this was simply to correlate multiple events per user.  As I developed new queries the number of alerts I received grew drastically.  There was really no logic other than looking for multiple events which simply led to noise.  I then sat down and thought about the reasons why I would want to be notified and came up with:

User has been identified in multiple rules + single phase + weight threshold met (500)
User has been identified in multiple phases + weight threshold met (300)
User exceeds weight threshold (1000)

To describe this logic in numbers it would look like:
|where ((scount > 1 and TotalWeight > 500) OR (pcount > 1 and TotalWeight > 300) OR (TotalWieght > 1000))

By implementing those 3 requirements I was able to eliminate the vast majority of noise and began surfacing interesting behavior.  I did  wonder what I may be missing though.  Were there users exhibiting behavior  that I would potentially want to investigate?  Obviously my alert logic wouldn't identify everything of interest.  I needed a good way to hunt for threads to pull, so  I set about describing behaviors in numbers.  I wrote about this a little in a previous post http://findingbad.blogspot.com/2020/05/its-all-in-numbers.html, but I'll go through it again.

Using the existing detections rules I used the rule name, weight and phase fields to create metadata that would describe a user's behavior.  Here's the fields I created and use for them:

Total Weight - The sum weight of a user's behavior.
Distinct rule count - The number of unique rule names a user has been seen in.
Total rule count - The total number of rules a user has been seen in.
Phase count - The number of phases a user has been seen in.

Knowing that riskier behavior often involves multiple actions taken by a user, I created the following fields to convey this.

Phase multiplier - Additional value given to a user that is seen in multiple phases.  Increases for every phase above 1.
Source multiplier - Additional value given to a user that is seen in multiple rules.  Increases for every rule above 1.

We then add Total Weight + Total rule count + Phase multiplier + Source multiplier to get the users weighted score.

By generating these numbers we can, not only observe how a user acted over the course of that day, but also surface anomalous behavior when compared to how others users acted.  For this I'm using an isolation forest and feeding it the total weight, phase count, total rule count and weighted numbers.  I feel these values best describe how a user acted and therefore are best used to identify anomalous activity. 

I'm also storing this metadata so that I can:

Look at their behavior patterns over time This particular user was identified on 4 different days:

Compare their sum of activity to other anomalous users.  This will help me identify the scale of their behavior.  This user's actions are leaning outside of normal ptterns:

I can also look at the daily activity and compare that against top anomalous users or where they rank as a percentage.  You can see on the plot below that the user's actions were anomalous on a couple of different days. 

There are also a number of other use cases for retaining this metadata.

This has taken a lot of time and effort to get to this point and is still a work in progress.  I can say though that I have found this to be a great way to quickly identify the threads that need be pulled.

Again, I'm sharing this so that maybe a conversation will begin to happen.  Are orgs hunting for insiders, and if so, how?  It's a conversation that's long overdue in my opinion.

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