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A Semi-Autonomic Framework for Intrusion Tolerance in Heterogeneous Networks

  • Salvatore D’Antonio
  • Simon Pietro Romano
  • Steven Simpson
  • Paul Smith
  • David Hutchison
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5343)

Abstract

A suitable strategy for network intrusion tolerance— detecting intrusions and remedying them—depends on aspects of the domain being protected, such as the kinds of intrusion faced, the resources available for monitoring and remediation, and the level at which automated remediation can be carried out. The decision to remediate autonomically will have to consider the relative costs of performing a potentially disruptive remedy in the wrong circumstances and leaving it up to a slow, but more accurate, human operator. Autonomic remediation also needs to be withdrawn at some point – a phase of recovery to the normal network state.

In this paper, we present a framework for deploying domain-adaptable intrusion-tolerance strategies in heterogeneous networks. Functionality is divided into that which is fixed by the domain and that which should adapt, in order to cope with heterogeneity. The interactions between detection and remediation are considered in order to make a stable recovery decision. We also present a model for combining diverse sources of monitoring to improve accurate decision making, an important pre-requisite to automated remediation.

Keywords

Intrusion Detection Anomaly Detection Intrusion Detection System Edge Router Decision Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Salvatore D’Antonio
    • 1
  • Simon Pietro Romano
    • 2
  • Steven Simpson
    • 3
  • Paul Smith
    • 3
  • David Hutchison
    • 3
  1. 1.CINI – ITeM LaboratoryNapoliItaly
  2. 2.University of Napoli “Federico II”NapoliItaly
  3. 3.Computing Department, InfoLab21Lancaster UniversityLancasterUK

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