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PREDICTING ZERO-DAY SOFTWARE VULNERABILITIES THROUGH DATA MINING

Su Zhang

Department of Computing and Information Science

Kansas State University

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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

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

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.

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

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

IE WinXP FireFox …

Target Machine

Scanner (e.g. Nessus or OVAL)

Our Prediction Model

Output(MTTNV&CVSS Metrics)

CPE (common platform enumeration)

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PREDICTION MODEL

Predictive data: CPE (common platform enumeration) 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? 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 )

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

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

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.

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 [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