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Security Event Data in the OODA Loop Model

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Martin Holste

on 16 September 2015

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Transcript of Security Event Data in the OODA Loop Model

Martin Holste, FireEye, Inc.
Deeper with OODA
Office of Personnel Management Hack:
22 million records stolen, CN implicated.
Thinking About Thinking
What do the outages at United Airlines, the New York Stock Exchange, and the Wall Street Journal all have in common?
Security Event Data in the OODA Loop Model
Action Items:
Read about attacks on others in your industry, be ready for what they encountered.
Are you collecting the right data?
Do you have a way to orient the data when it comes in?
Do you have the right mental (threat) models?
Do you have the tech and political capacity to act on decisions?
Orientation: Building Mental Models
Factors affecting orientation:
Quality of current mental models
Efficacy of previous observations
How do we limit cognitive bias?
Hypothetical Scenario:
United/NYSE/WSJ are down, our email is down. Are these events connected?
Cognitive Bias in Information Security
Great work done by Chris Sanders (@chrissanders88): "Defeating Cognitive Bias on Developing Analytic Technique"
Basic OODA
The OODA Loop is well-known and has been used as a framework for business, athletics, and many other forms in which there is an adversary.
OODA is present in incident response at many levels
Packet >
dstport 25
login jsmith
Inter-loop Feedback
Raw data
of adversary
OODA is often oversimplified.
It is about shifting mental models to better acclimate to new realities.
Boyd inferred that any logical model of reality is incomplete (and possibly inconsistent) and must be continuously refined/adapted in the face of new observations.

New data is always required to continually validate conclusions.
Gödel’s Incompleteness Theorems
Heisenberg’s Uncertainty Principle
Even as we get more precise observations about a particular domain, we’re likely to experience more uncertainty about another.

As we find out more about what is happening, it raises more questions.
2nd Law of Thermodynamics
Lack of new information about the environment creates a “closed system" which leads to high entropy in the system (chaos).

Information starvation will lead to confusion.
OODA is about constantly reevaluating current understandings to ensure the most effective mental models are constructed.
Mental models are contained in the orientation phase, which is why it is the most important.
Why do we have bias?
Adaptive Bias: Theory that humans favor making decisions that lower the cost of being wrong versus the number of times they are wrong.
Costly information hypothesis: It is cheaper to learn from others than to figure things out for ourselves.
The OODA Loop was invented by USAF Colonel John Boyd in the 1960's.

Initially applied specifically to air combat, he lectured for many years on using it as a framework for winning in abstract contests.
So, is United/NYSE/WSJ related to our email outage?

Bias suggests yes, but evidence offered spear phishing theory as better model match.
Availability Bias
In incident response:
Conclusions from recent incidents are favored
Conclusions described by other responders are favored
Events that have happened recently and are "available" in memory seem related.
Collect more data to have more facts to operate on (increase new information)
Put data into context, make it convenient to access (more analysis)
Integrate previous incident data with new data (previous experiences)
Cultural traditions and genetic heritage have ingrained biases.

Use tools that account for cognitive bias by automating the parts of analysis that humans tend to apply bias against.
Meaningless anomalies: Anything wrong here?
Just Google
Same data minus Google:
Actually bad
Overall event rates also show a pattern:
Bad guys in big groups
with similar counts
Jenks natural breaks
classification (K-Means) groups our domain counts
Try a Different Analytical Approach
Boyd's Example of Building Mental Models
Take the skis
Take the motor
Take the handlebars
Take the treads
What do you get if you combine these pieces?
Back to the hypothetical email outage, what pieces might we have?
Event data showing:
User login to email
Search engine indexing the company directory
Company is unable to send email
SMTP event rate above average
We need to derive a mental model to describe the situation.
Hypothesis: Spear Phishing
The speed at which we can develop and test this hypothesis constitutes the speed of our OODA loop.
Outlier detection doesn't help us here.
Boyd: “A loser is someone who cannot build snowmobiles when facing uncertainty and unpredictable change"
Can spot periodicity in overall event volume with Fast Fourier Transforms
Basic search capability is a critical component to verifying assumptions. Coupled with analytics, it can be even more powerful.
By applying basic statistical analytics to search results, we create a hybrid in which the human analyst provides a starting point and the analytics provide clarity and validation of assumptions.
Real investigation:
IDS Alert for POWELIKS crimeware on and another at the same time on

Mental model (orientation): These are related incidents and are part of the same campaign. Verify.
Run two searches, one for A and one for B, then compare the result counts for each field-value (such as dstip) combination and show the intersection.
Scoring is simple (A + B) - abs(A - B). Tries to find highest counts that are similar between A and B.
Analytics can be misleading, we need to get a second opinion.
We can use multiple analytical methods on the same data to see if they agree. We can then follow-up by inspecting the final conclusion with search.
Looks like it correlates, does it? We can take the time series data and check it with sample correlation:
domain:ip-addr.es domain:mevtutorial.in 0.925
domain:ip-addr.es domain:motomiles.com 0.924
domain:ip-addr.es domain:mundofomix.com 0.923
domain:ip-addr.es domain:mobilecomputingtoday.com 0.922
domain:ip-addr.es domain:loverocksusa.com 0.920
domain:ip-addr.es domain:maoribooks.com 0.921
Yes, comparing peaks and valleys together shows strong correlation.
Collect this info
Know what web page is directory
Know email servers and have data describing failure
Analytics to find anomalous increase
OODA Requirements to Get and Use Data
Mental model (hypothesis) of spear phishing
Ability to test hypothesis
Search and analytics speed up the OODA loop by decreasing the time it takes to build mental models and test hypothesis.
If you get a quick mental model fit you get to skip the decide step entirely and go directly to action.
Time frame: YEARS! Initial breach in 2013.
OPM did not shift mental models in the face of an abrupt change. Had no means of updating models by focusing on patching.
How do you keep mental models fresh for responding to major attacks?

Performing incident response every day on common crimeware attacks will help ensure your tech and procedures are relevant, fresh, and effective.

Without regular exercise, your models will be out of date in the face of a serious incident.
Inter-OODA Feedback
Each success at a lower level is feedback to the upper level.
Successful incident response can lead to policy change.
At higher levels, OODA speed is less important than accurate mental models.
WHAT (artifacts)
WHO/WHY (attribution/motive)
Question-based Incident Response Model
Do you have proper incident response reporting?
Indicator extraction
Follow-up interviews
Are key decision makers aware of your findings to maintain and improve political capacity?
Don't leave this feedback loop open.
We need to get data, analyze it, and make a decision.
Most recognized aspect: Go from data to action as quickly as possible
Recon pulls company directory
Phishing emails are sent
User falls victim, creds stolen
Credentials used to spam, causing MX blacklist
(Answer: not much)
So why do we find ourselves automatically asking this question?
Use these find interesting data, then zoom in and verify with another search.
Start with seed IDS event to search, use multiple analytics on results:
Anomalous spike (linear regression)
Interesting groupings by count (Jenks natural breaks)
Similar peaks/valleys (sample correlation)
Interesting patterns (FFT)
Search-Analytic Cycle
Search results show actual packet and event data, decorated with WHOIS/GeoIP, threat intel, etc.
Analytics point out patterns, groupings, and features of interest in search data.
Each cycle refines the data until it conclusively supports or invalidates the hypothesis.
Removing the outlier makes this pattern visible.
About Me:
Senior Researcher at FireEye,
Co-Founder of Threat Analytics Platform
Author of open-source Enterprise Log Search and Archive (ELSA) and StreamDB projects.

Minor Airplane Enthusiast
Presentation Goals:
Understand how we think
Learn how this affects how we acquire and analyze data
Use this knowledge to improve our incident response capabilities
It draws on fundamental laws of math and science.
New ELSA transform:
cluster(<field>, [ <num groups> ])


sig_msg:POWELIK groupby:srcip | subsearch(method:POST) | cluster(dstip) | sum(cluster)
A few ELSA Updates:
Github is going great, THANKS to all contributors!
Opallios, Inc. contributing improvements to graph lib, ingest methods, and aggregation functions
Upcoming release of new search engine with ELSA plugin
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