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

Submitted by drwright on Thu, 03/10/2016 - 10:35am

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

  • With the increase in popularity and use of Android apps, security and privacy issues of Android apps are becoming a concern.  We have begun a project in which we aim to predict vulnerability and privacy risk score for each version of apps from different features such as functional complexity, number of classes, number of lines per code, and number of functions.  The dataset used in the project is a database containing software related metrics data and vulnerability data of 1179 open source Android apps that have 4416 different versions.

 

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