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STRAIGHT TALK ON MACHINE LEARNING
WHAT THE MARKETING DEPARTMENT DOESN’T WANT YOU
TO KNOW
DR. SVEN KRASSER CHIEF SCIENTIST
@SVENKRASSER
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
MACHINE LEARNING AT CROWDSTRIKE
§ ~50 billion events per day
§ ~800 thousand events per second peak
§ ~700 trillion bytes of sample data
§ Local decisions on endpoint and large scale analysis in cloud
§ Static and dynamic analysis techniques, various rich data sources
§ Analysts generating new ground truth 24/7
BRIEF ML
PRIMER
Height [mm]
Weight[10-1kg]
• What’s this?
http://tinyurl.com/MLprimer
• Two features
• Two classes
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
BETTER
FEATURES
“Buttock Circumference” [mm]
Weight[10-1kg]
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
MODEL
FIT
“Buttock Circumference” [mm]
Weight[10-1kg]
• Support Vector
Machine
• Real world:
more features
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
“Buttock Circumference” [mm]
Weight[10-1kg]
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
LET’S
CLASSIFY
“Buttock Circumference” [mm]
Weight[10-1kg]
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
LET’S
CLASSIFY
“Buttock Circumference” [mm]
Weight[10-1kg]
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
LET’S
CLASSIFY
“Buttock Circumference” [mm]
Weight[10-1kg]
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
LET’S
CLASSIFY
“Buttock Circumference” [mm]
Weight[10-1kg]
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
LET’S
CLASSIFY
• Get more “blue”
right (true positives)
• Get more “red”
wrong (false
positives)
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
RECEIVER
OPERATING
CHARACTERISTICS
CURVE
False Positive Rate
TruePositiveRate
Detect	more	by	accepting	more	false	positives
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
MORE
DIMENSIONS
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
• Some 160
dimensions
• Projected back to 2-
dimensional screen
• Perfect separation
CURSE OF DIMENSIONALITY
REDUCED
predictive performance
INCREASED
training time
SLOWER
classification
LARGER
memory footprint
Source: https://commons.wikimedia.org/w/index.php?curid=2257082
Source: https://commons.wikimedia.org/w/index.php?curid=2257082
Height (mm)
Weight[10-1kg]
DIMENSIONALITY
AND SPARSENESS
2016	CrowdStrike,	Inc.	All	rights	reserved.
Height (mm)
Weight[10-1kg]
DIMENSIONALITY
AND SPARSENESS
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
ML IN INFOSEC APPLICATIONS
§ Not a single model solving everything
§ But many models working on the data in scope
§ Endpoint vs cloud
§ Fast response vs long observation
§ Lean vs resource intensive
§ Effectiveness vs interpretability
§ Avoid ML blinders
§ The guy in your store at 2am wielding a crowbar is not a customer
FILE
ANALYSIS
AKA Static Analysis
• THE GOOD
– Relatively fast to detect malware
– Scalable
– No need to detonate (“pre-execution”)
– Platform independent, can be done at endpoint or cloud
• THE BAD
– Limited insight due to narrow view
– Different file types require different techniques
– Different subtypes need special consideration
– Packed files
– .Net
– Installers
– EXEs vs DLLs
– Obfuscations (yet good if detectable)
– Ineffective against exploitation and malware-less attacks
– Asymmetry: a fraction of a second to decide for the
defender, months to craft for the attacker
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
VIRUSTOTAL INTEGRATION
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
ENGINEERED FEATURES
FOR EXECUTABLE FILES
32/64BIT
EXECUTABLE
SUBSYSTEM
TYPE
MACHINE
INSTRUCTION
DISTRIBUTION
FILESIZE TIMESTAMP
DEBUG
INFORMATION
PRESENT
PACKERTYPE FILEENTROPY
NUMBEROF
SECTIONS
NUMBER
WRITABLE
SECTIONS
NUMBER
READABLE
SECTIONS
NUMBER
EXECUTABLE
SECTIONS
DISTRIBUTION
OFSECTION
ENTROPY
IMPORTED
DLLNAMES
IMPORTED
FUNCTION
NAMES
COMPILER
ARTIFACTS
LINKER
ARTIFACTS
RESOURCE
DATA
PROTOCOL
STRINGS
IPS/DOMAINS
PATHS
PRODUCT
METADATA
DIGITAL
SIGNATURE
ICON
CONTENT
…
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
ENGINEERED FEATURES
FOR EXECUTABLE FILES
32/64BIT
EXECUTABLE
SUBSYSTEM
TYPE
MACHINE
INSTRUCTION
DISTRIBUTION
FILESIZE TIMESTAMP
DEBUG
INFORMATION
PRESENT
PACKERTYPE FILEENTROPY
NUMBEROF
SECTIONS
NUMBER
WRITABLE
SECTIONS
NUMBER
READABLE
SECTIONS
NUMBER
EXECUTABLE
SECTIONS
DISTRIBUTION
OFSECTION
ENTROPY
IMPORTED
DLLNAMES
IMPORTED
FUNCTION
NAMES
COMPILER
ARTIFACTS
LINKER
ARTIFACTS
RESOURCE
DATA
PROTOCOL
STRINGS
IPS/DOMAINS
PATHS
PRODUCT
METADATA
DIGITAL
SIGNATURE
ICON
CONTENT
…
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
• Continuous features
• Categorical features
• n-hot encoding
• Embedding
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
LEARNED
FEATURES
• Unstructured file
content
• Algorithm uncovers
interesting
properties
• Requires a lot more
more input data
• Unlocks more
insight
String-based feature
Executablesectionsize-based
feature
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
COMBINING
FEATURES
Subspace Projection A
SubspaceProjectionB
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
COMBINING
FEATURES
CLASSIFICATION
PERFORMANCE
99%DETECTIONRATE
1%FALSEPOSITIVES
Malware?
Malware
99%DETECTIONRATE
1%FALSEPOSITIVES
99%DETECTIONRATE
1%FALSEPOSITIVES
Not	Malware
99% TRUE POSITIVE RATE
31
Chanceofatleastone
successforadversary
Number of attempts
1%
>99.3%
500
2017	CROWDSTRIKE,	INC.	ALL	RIGHTS	RESERVED.	
Training set distribution generally differs from…
DIFFERENCE IN DISTRIBUTIONS
§ Real-world distribution (customer networks)
§ Evaluations (what customers test)
§ Testing houses (various 3rd party testers with varying methodologies)
§ Community resources (e.g. user submissions to CrowdStrike scanner on
VirusTotal)
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
FIELD
DISTRIBUTIONClean Malware	
Type	A
Malware	
Type	B
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
FIELD
DISTRIBUTION
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
TEST
DISTRIBUTION
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
COMBINED DATA
• More accurate
• Fares better on test
data
• But: not as good on
field data
MALWARE
40%
THREAT
SOPHISTICATION
MALWARE
STOPPING
MALWARE
IS NOT
ENOUGH
HARDERTOPREVENT
&DETECT
LOW
HIGH
HIGH
LOW
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
THREAT
SOPHISTICATION
MALWARE
NON-MALWARE
ATTACKS
MALWARE
40%
NATION-
STATES
60%
NON-MALWARE
ATTACKS
ORGANIZED
CRIMINAL GANGS
HACKTIVISTS/
VIGILANTES
TERRORISTS CYBER-
CRIMINALS
YOU NEED COMPLETE
BREACH
PREVENTION
HARDERTOPREVENT
&DETECT
LOW
HIGH
HIGH
LOW
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
Next-Generation Endpoint Protection
Cloud Delivered. Enriched by Threat Intelligence
MANAGED
HUNTING
ENDPOINT DETECTION
AND RESPONSE
NEXT-GEN
ANTIVIRUS
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
KEY
POINTS
• Machine Learning is an effective tool against
unknown threats
• Trading off true positives and false positives
• Features matter, but don’t count them
• One of many uses is static analysis
• Detecting 99% malware means an APT has a
100% chance of getting malware into your
environment
• For ML, distributions matter
• The majority of intrusions are not malware-
based
• Avoid silent failure
• Use a comprehensive array of techniques
2016 CROWDSTRIKE, INC. ALL RIGHTS RESERVED.
Straight Talk on Machine Learning -- What the Marketing Department Doesn’t Want You to Know

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