Visible to the public Vulnerability and Resilience Prediction Models - October 2016Conflict Detection Enabled

Submitted by drwright on Wed, 09/14/2016 - 10:46am

Public Audience
Purpose: To highlight project progress. Information is generally at a higher level which is accessible to the interested public. All information contained in the report (regions 1-3) is a Government Deliverable/CDRL.

PI(s):  Mladen Vouk, Laurie Williams
Researchers:  Donghoon Kim, Akond Rahman

HARD PROBLEM(S) ADDRESSED

  • Security Metrics and Models
  • Resilient Architectures
  • Scalability and Composability

Resilience of software to attacks is an open problem. Resilience depends on the science behind the approach used, as well as on our engineering abilities. The scope includes recognition of attacks through metrics and models we use to describe and identify software vulnerabilities, and the models we use to predict resilience to attacks in the field (Security Metrics and Models). It also depends on the software (and system) architecture(s) used (Resilient Architectures), and their scalability (Scalability and Composability). For example, if one has a number of highly attack-resilient components and appropriate attack sensors, is it possible to compose a resilient system from these parts, and how does that solution scale and age?

Additionally, vulnerability prediction models can be used to prioritize security-related validation and verification efforts to the most risky parts of a project.  Prior studies have shown how software related metrics can be used to predict software defects. We draw inspiration from these studies and identify the possibility of applying data mining techniques to predict vulnerability.

PUBLICATIONS

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ACCOMPLISHMENT HIGHLIGHTS

  • Existing high-assurance software engineering experiences with rare defects (e.g., in the safety space) can be followed to avoid, eliminate and tolerate security vulnerabilities. Security problem avoidance, elimination and fault tolerance strategies need to apply existing (and enhanced) safety related knowledge and princples in specifying and guiding security sensitive software development, testing, acquisition, use, and maintenance.

 

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