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

Submitted by drwright on Tue, 12/01/2015 - 1:22pm

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

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?

PUBLICATIONS

  • Two pending publications (see group internal report)

ACCOMPLISHMENT HIGHLIGHTS

  • Modern workflows have many points where they touch internet, clouds, and numerous static and mobile devices. They may be composed of complex chains of distributed applications and data sources and data sinks, and may incorporate a number of IoT devices. Making such complex entities resilient to cyber attacks presents a special challenge. Workflows, and workflow components, need to be self-healing and pro-actively resilient. We are working on a resilience model that integrates (IoT) sensor-based attack detectors into complex cloud-based workflows.

(v4)

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