• DocumentCode
    2128744
  • Title

    Run-Time Risk Management in Adaptive ICT Systems

  • Author

    Surridge, Mike ; Nasser, Bassem ; Chen, Xia ; Chakravarthy, Animesh ; Melas, Panos

  • Author_Institution
    IT Innovation Centre Gamma House, Southampton, UK
  • fYear
    2013
  • fDate
    2-6 Sept. 2013
  • Firstpage
    102
  • Lastpage
    110
  • Abstract
    We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers.
  • Keywords
    inference mechanisms; risk management; security of data; A-CDM scenario; Bayesian inference; ISO 27000 type approach; SERSCIS project; adaptive multistakeholder ICT systems; airport collaborative decision making; automated design-time threat identification; automated design-time threat mitigation analysis; dynamic risk management approach; multiple airport service providers; risk mitigation strategy; run-time risk management; run-time system monitoring data; semantic reasoning; semantic threat models; Availability; Security; adaptive systems; machine reasoning; risk management; secure ICT;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Availability, Reliability and Security (ARES), 2013 Eighth International Conference on
  • Conference_Location
    Regensburg
  • Type

    conf

  • DOI
    10.1109/ARES.2013.20
  • Filename
    6657231