Model-based diagnosis for information survivability

  • Authors:

  • Howard Shrobe

  • Affiliations:

  • Artificial Intelligence Laboratory, Massacusetts Institute of Technology, Cambridge, MA

  • Venue:
  • IWSAS'01 Proceedings of the 2nd international conference on Self-adaptive software: applications
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

Abstract

The Infrastructure of modern society is controlled by software systems that are vulnerable to attack. Successful attacks on these systems can lead to catastrophic results; the survivability of such information systems in the face of attacks is therefore an area of extreme importance to society. This paper presents model-based techniques for the diagnosis of potentially compromised software systems; these techniques can be used to aid the self-diagnosis and recovery from failure of critical software systems. It introduces Information Survivability as a new domain of application for model-baesed diagnosis and it presents new modeling and reasoning techniques relevant to the domain. In particular: 1) We develop techniques for the diagnosis of compromised software systems (previous work on model-base diagnosis has been primarily cconcerned with physical components); 2) We develop methods for dealing with model-based diagnosis as a mixture of symbolic and Bayesian inference; 3) We develop techniques for dealing with common-mode failures; 4) We develop unified representational techniques for reasoning about information attacks, the vulnerabilities and compromises of computational resources, and the observed behavior of computations; 5) We highlght additional information that should be part of the goal of modelbased diagnosis.