Accession Number:

ADA633622

Title:

Predicting Attack-prone Components with Source Code Static Analyzers

Descriptive Note:

Doctoral thesis

Corporate Author:

NORTH CAROLINA STATE UNIV AT RALEIGH

Personal Author(s):

Report Date:

2009-05-01

Pagination or Media Count:

122.0

Abstract:

No single vulnerability detection technique can identify all vulnerabilities in a software system. However, the vulnerabilities that are identified from a detection technique may be predictive of the residuals. We focus on creating and evaluating statistical models that predict the components that contain the highest risk residual vulnerabilities.

Subject Categories:

  • Computer Programming and Software
  • Computer Systems Management and Standards

Distribution Statement:

APPROVED FOR PUBLIC RELEASE