Download Predicting zero-day software vulnerabilities through data mining Su

PREDICTING ZERO-DAY
SOFTWARE VULNERABILITIES
THROUGH DATA MINING
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Su Zhang
Department of Computing and Information Science
Kansas State University
OUTLINE
Motivation.
 Related work.
 Proposed approach.
 Possible techniques.
 Plan.

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OUTLINE
Motivation.
 Related work.
 Proposed approach.
 Possible techniques.
 Plan.

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THE TREND OF VULNERABILITY NUMBERS
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ZERO-DAY VULNERABILITY
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What is zero-day vulnerability?
It is a vulnerability which is found by underground hackers
before being made public.
Increasing threat from zero-day vulnerabilities.
Many attacks are attributed to zero-day vulnerabilities.
E.g. in 2010 Microsoft confirmed a vulnerability in Internet
Explorer, which affected some versions that were released
in 2001.
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OUR GOAL
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Risk awareness. The possibility of zero-day
vulnerability must be considered for
comprehensive risk assessment for enterprise
networks.
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ENTERPRISE RISK ASSESSMENT FRAMEWORK
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ENTERPRISE RISK ASSESSMENT FRAMEWORK
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ENTERPRISE RISK ASSESSMENT FRAMEWORK
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ENTERPRISE RISK ASSESSMENT FRAMEWORK
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ENTERPRISE RISK ASSESSMENT FRAMEWORK
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PROBLEM
Predict
the information of
zero – day vulnerabilities
from software configurations.
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OUTLINE
Motivation.
 Related work.
 Proposed approach.
 Possible techniques.
 Plan.

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RELATED WORK

O. H. Alhazmi and Y. K. Malaiya, 2005.

Andy Ozment, 2007.

Kyle Ingols, et al, 2009.
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Miles A. McQueen, et al, 2009.
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OUTLINE
Motivation.
 Related work
 Proposed approach.
 Possible techniques.
 Plan.

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PROPOSED APPROACH

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Predict the likelihood of zero-day vulnerabilities
for specific software applications.
NVD
Available since 2002.
 Rich data source including the preconditions and
consequences of vulnerabilities. It could be used to
build our model and validate our work.

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SYSTEM ARCHITECTURE
Output(MTTNV&CVSS Metrics)
Our Prediction Model
CPE (common platform enumeration)
Scanner (e.g. Nessus or OVAL)
Target Machine
IE
WinXP
FireFox
…
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PREDICTION MODEL

Predictive data: CPE (common platform
enumeration)

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Indicate software configuration on a host.
Predicted data: MTTNV (Mean Time to Next
Vulnerability) & CVSS Metrics
MTTNV indicates the probability of zero-day
vulnerabilities.
 CVSS metrics indicate the properties of the predicted
vulnerabilities.

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CPE (COMMON PLATFORM ENUMERATION)

What is CPE?

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CPE is a structured naming scheme for information
technology systems, software, and packages.
Example (in primitive format)

cpe:/a:acme:product:1.0:update2:pro:en-us
Professional edition of the "Acme Product 1.0 Update
2 English".
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CPE LANGUAGE
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CVSS (COMMON VULNERABILITY SCORING SYSTEM )

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An open framework for communicating the
characteristics and impacts of IT vulnerabilities.
Metric Vector
access complexity (H, M, L)
authentication ( R, NR)
confidentiality (N, P, C)
...
CVSS Score: Calculated based on above vector. It
indicates the severity of a vulnerability.
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CVSS USED IN RISK ASSESSMENT

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We use CVSS to derive a conditional probability.
How likely a vulnerability could be successfully
exploited, given all preconditions fulfilled.
By combining the conditional probability with
attack graph one can calculate the cumulative
probability, we could obtain a overall estimated
likelihood of the given machine being
compromised.
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OUTLINE
Motivation.
 Related work.
 Proposed approach.
 Possible techniques.
 Plan.

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POSSIBLE TECHNIQUES

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Linear Regression ( input are continuous
variables).
Statistical classification (input are discrete
variables).
Maximum likelihood and least squares
(Determining the parameters of our model).
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VALIDATION METHODOLOGY

Earlier years of NVD: Building our model.
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Later years of NVD: Validate our model.

Criteria: Closer to the factual value than without
considering zero-day vulnerabilities.
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OUTLINE
Motivation.
 Related work.
 Proposed approach.
 Possible techniques.
 Plan.

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PLAN

Next phase: Study data-mining tools (e.g.
Support Vector Machine) . Then build up our
prediction model.


Validate the model on NVD.
Final phase:
If the previous phase provides a good model, we will
incorporate the generated result into MulVAL.
 Otherwise, we are going to investigate the problem.

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REFERENCES
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[1]Andrew Buttner et al, ”Common Platform Enumeration (CPE) –
Specification,” 2008.
[2]NVD, http://nvd.nist.gov/home.cfm.
[3]O. H. Alhazmi et al, “Modeling the Vulnerability Discovery Process,” 2005.
[4]Omar H. Alhazmi et al, “Prediction Capabilities of Vulnerability Discovery
Models,” 2006.
[5]Andy Ozment, “Improving Vulnerability Discovery Models,” 2007.
[6]R. Gopalakrishna and E. H. Spafford, “A trend analysis of vulnerabilities,”
2005.
[7]Christopher M. Bishop, “Pattern Recognition andMachine Learning,” 2006.
[8]Xinming Ou et al, “MulVAL: A logic-based network security analyzer,” 2005.
[9] Kyle Ingols et al, “Modeling Modern Network Attacks and
Countermeasures Using Attack Graphs” 2009.
[10] Miles A. McQueen et al, “Empirical Estimates and Observations of 0Day
Vulnerabilities,” 2009.
[11] Alex J. Smola et al, “A Tutorial on Support Vector Regression,” 1998.
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THANK YOU!
Questions & Answers
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