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Catching Insider Data Theft with Stochastic Forensics 2012

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Catching Insider Data Theft with Stochastic Forensics Jonathan Grier
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Page 1: Catching Insider Data Theft with Stochastic Forensics 2012

CatchingInsider

Data Theftwith

Stochastic ForensicsJonathan Grier

Page 2: Catching Insider Data Theft with Stochastic Forensics 2012

ConfidentialityTo preserve client confidentiality,case information (names, places, dates, andsettings) has been omitted or altered.

The data and techniques presentedhave not been altered.

Page 3: Catching Insider Data Theft with Stochastic Forensics 2012

Can you find the data thief?

Page 4: Catching Insider Data Theft with Stochastic Forensics 2012

Harlan Carvey, Windows Forensic Analysis, 2009

Page 5: Catching Insider Data Theft with Stochastic Forensics 2012

Harlan Carvey, Windows Forensic Analysis, 2009

No Artifacts = No Forensics

Page 6: Catching Insider Data Theft with Stochastic Forensics 2012

Harlan Carvey, Windows Forensic Analysis, 2009

No Artifacts = No Forensics???

Page 7: Catching Insider Data Theft with Stochastic Forensics 2012
Page 8: Catching Insider Data Theft with Stochastic Forensics 2012

Access timestamps updates during:

Routine access

Page 9: Catching Insider Data Theft with Stochastic Forensics 2012
Page 10: Catching Insider Data Theft with Stochastic Forensics 2012

Access timestamps updates during:

Copying a folder Routine access

Page 11: Catching Insider Data Theft with Stochastic Forensics 2012

Copying Folders Routine Access

Nonselective All subfolders and files accessed

Selective

Temporally continuous Temporally irregular

Recursive Random order

Directory accessed before its files

Files can be accessed without directory

Page 12: Catching Insider Data Theft with Stochastic Forensics 2012

COPIEDNOT COPIED

Page 13: Catching Insider Data Theft with Stochastic Forensics 2012

“slap-your-head-and-say-'doh-wish-I'd-thought-of-that’”-- an anonymous reviewer

No ArtifactsYes Forensics

Page 14: Catching Insider Data Theft with Stochastic Forensics 2012

Not so fast...

1. Timestamps are overwritten very quickly

2. There are other nonselective, recursive . activities (besides copying)

Page 15: Catching Insider Data Theft with Stochastic Forensics 2012

Not so fast...

1. Timestamps are overwritten very quickly

Can we use this method months later?

On a heavily used system?

Won’t most of the timestamps have been overwritten?

Page 16: Catching Insider Data Theft with Stochastic Forensics 2012

Not so fast...

1. Timestamps are overwritten very quickly

Can we use this method months later?

On a heavily used system?

Won’t most of the timestamps have been overwritten?

YES!

YES!

Notreally!

Page 17: Catching Insider Data Theft with Stochastic Forensics 2012

Two observations:

1. Timestamps values can increase,but never decrease.

2. A lot of files just collect dust.Most activity is on a minority of files.

Page 18: Catching Insider Data Theft with Stochastic Forensics 2012

Farmer & Venema, Forensic Discovery, 2005

Page 19: Catching Insider Data Theft with Stochastic Forensics 2012

At tcopying:• All files have access_timestamp = tcopying

Page 20: Catching Insider Data Theft with Stochastic Forensics 2012

At tcopying:• All files have access_timestamp = tcopying

Several weeks later:• All files have access_timestamp ≥ tcopying

Page 21: Catching Insider Data Theft with Stochastic Forensics 2012

At tcopying:• All files have access_timestamp = tcopying

Several weeks later:• All files have access_timestamp ≥ tcopying• Many files still have access_timestamp = tcopying

Page 22: Catching Insider Data Theft with Stochastic Forensics 2012

After 300 days of simulated activity

Histogram of access timestamps

Page 23: Catching Insider Data Theft with Stochastic Forensics 2012
Page 24: Catching Insider Data Theft with Stochastic Forensics 2012

Copying creates a

cutoff clustercutoff – No file has timestamp < tclustercluster – Many files have timestamp = tcluster

Page 25: Catching Insider Data Theft with Stochastic Forensics 2012

Aren’t there other recursive access patterns besides copying?

Affirming theconsequentA ⟶ B doesn’t prove B⟶A.

The absence of a cutoff cluster can disprove copying, but the existencecan’t prove copying.

Perhaps they ran grep.

Page 26: Catching Insider Data Theft with Stochastic Forensics 2012

Indeed, there are!

vs.Affirming theconsequentA ⟶ B doesn’t prove B⟶A.

Abductive reasoningAn unusual observation

supports inferring alikely cause.

The absence of a cutoff cluster can disprove copying, but the existencecan’t prove copying.

Who’s trying to prove anything?

Investigate! One clue leads to another until the case unravels.

Perhaps they ran grep.Indeed!Check if grep is installed, if they’ve ever run it before, or after, on any folder.Check why they were still in the building at 11 PM.

Page 27: Catching Insider Data Theft with Stochastic Forensics 2012
Page 28: Catching Insider Data Theft with Stochastic Forensics 2012

Farmer & Venema, Forensic Discovery, 2005

Page 29: Catching Insider Data Theft with Stochastic Forensics 2012

An actual investigation...

Page 30: Catching Insider Data Theft with Stochastic Forensics 2012

Part II:

Now for the real world...

Page 31: Catching Insider Data Theft with Stochastic Forensics 2012

NOISE

Page 32: Catching Insider Data Theft with Stochastic Forensics 2012

OpenSolaris cp command source code

Page 33: Catching Insider Data Theft with Stochastic Forensics 2012

Notice anything?

Page 34: Catching Insider Data Theft with Stochastic Forensics 2012

Notice anything?

Page 35: Catching Insider Data Theft with Stochastic Forensics 2012

OpenSolaris cp command source codewritefile() function

Page 36: Catching Insider Data Theft with Stochastic Forensics 2012
Page 37: Catching Insider Data Theft with Stochastic Forensics 2012

Is all lost(on Windows at least)

?

Page 38: Catching Insider Data Theft with Stochastic Forensics 2012
Page 39: Catching Insider Data Theft with Stochastic Forensics 2012

a Directoryis also a File!

Page 40: Catching Insider Data Theft with Stochastic Forensics 2012

Filter...

Page 41: Catching Insider Data Theft with Stochastic Forensics 2012

NOISE

Page 42: Catching Insider Data Theft with Stochastic Forensics 2012

ACCURACY?

Page 43: Catching Insider Data Theft with Stochastic Forensics 2012

ACCURACY?Who needs

Page 44: Catching Insider Data Theft with Stochastic Forensics 2012

Part III:

ApplyingStochasticForensics

Page 45: Catching Insider Data Theft with Stochastic Forensics 2012

Eyeball?

Page 46: Catching Insider Data Theft with Stochastic Forensics 2012

Filter&

Plot

Page 47: Catching Insider Data Theft with Stochastic Forensics 2012

Filter1. By folder

Page 48: Catching Insider Data Theft with Stochastic Forensics 2012

Filter1. By folder2. Directories versus Files

Page 49: Catching Insider Data Theft with Stochastic Forensics 2012

Filter1. By folder2. Directories versus Files3. Permissions

Page 50: Catching Insider Data Theft with Stochastic Forensics 2012

Filter1. By folder2. Directories versus Files3. Permissions4. Other

Page 51: Catching Insider Data Theft with Stochastic Forensics 2012

PlotOur visual cognition isamazingly robust

Ploticus: http://ploticus.sourceforge.net

Page 52: Catching Insider Data Theft with Stochastic Forensics 2012

Interpret&

Advance

Page 53: Catching Insider Data Theft with Stochastic Forensics 2012

No Cluster?

Strong evidence of no copying

Page 54: Catching Insider Data Theft with Stochastic Forensics 2012

Found Cluster?

1. Check control folders2. Search for causes3. Fingerprint it

Page 55: Catching Insider Data Theft with Stochastic Forensics 2012

Found Cluster?

A cluster defines a tight window of opportunity.

Use it to propel the investigation forward.

Page 56: Catching Insider Data Theft with Stochastic Forensics 2012

Part IV:

ForensicHacking

Page 57: Catching Insider Data Theft with Stochastic Forensics 2012

hack v.Exploring the inner workings of somethingby using it in a way its creators never imagined.

Page 58: Catching Insider Data Theft with Stochastic Forensics 2012

Look at theSurviving Data

ReconstructPrevious Data

This previous data is our deliverable.

Classical Forensics:

Page 59: Catching Insider Data Theft with Stochastic Forensics 2012

What do I want to know about?

What behavior is associated?

How does that behavior affect the system?

Measure those effects.Draw a (quantifiable) inference.

Look at theSurviving Data

ReconstructPrevious Data

This previous data is our deliverable.

Classical Forensics:

Stochastic Forensics:

Page 60: Catching Insider Data Theft with Stochastic Forensics 2012

Leading researchers have called to move from:“What data can we find?”

To:“What did this person do?”

Page 61: Catching Insider Data Theft with Stochastic Forensics 2012
Page 62: Catching Insider Data Theft with Stochastic Forensics 2012

Farmer & Venema, Forensic Discovery, 2005

Page 63: Catching Insider Data Theft with Stochastic Forensics 2012

Research Agenda(i.e. a request for help)

1. Scientific testingAutomate, build corpus, confidence levels, validate

2. FingerprintingWe can distinguish copying from grep!

3. Probability value

4. What other questions can stochastic forensics address?Let’s find sloppy questionsand answer them less precisely!

Page 64: Catching Insider Data Theft with Stochastic Forensics 2012

Questions?Comments?

Want More Info?

Please speak to me,here at Black Hat

or jdgrier at grierforensics com.


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