skip to main content
10.1145/3307772.3330157acmotherconferencesArticle/Chapter ViewAbstractPublication Pagese-energyConference Proceedingsconference-collections
poster

Risk-based Decision-Support for Vulnerability Remediation in Electric Power Networks

Published: 15 June 2019 Publication History

Abstract

Power grids are becoming increasingly intelligent. However, the boundaries between their operational technology (OT) environments and the (vulnerable) IT networks tend to dissolve. In such systems, several technical and economic factors can significantly affect the upgrading decisions. These factors include, just to name a few, limited time and budget as well as legal constraints. To cope with these challenges, an involved decision maker has to prudently prioritize the possible vulnerability remediation actions. The key objective of prioritization decisions is to efficiently reduce the inherent security risk to which the system in question is exposed. Due to the critical role of power systems, their decision makers tend to enhance the system resilience against extreme events. That is, they seek to avoid decision options associated with extreme (adverse) consequences. Therefore, we propose an integrated risk-based decision-support methodology for prioritizing risk remediation activities. It leverages (i) the Time-To-Compromise (TTC) metric to quantitatively assess the security risk, and (ii) a game-theoretical model to support the decision-making process. The game model considers carefully the specific risk attitude of the decision makers involved in the patch management process across electric power organizations.

References

[1]
Ali Alshawish, Mohamed Amine Abid, and Hermann de Meer. 2018. Game-Theoretic Optimization for Physical Surveillance of Critical Infrastructures: A Case Study. In Game Theory for Security and Risk Management. Springer, 353--389.
[2]
Ali Alshawish, Mohamed Amine Abid, Hermann de Meer, Stefan Schauer, Sandra König, Antonios Gouglidis, and David Hutchison. 2018. G-DPS: A game-theoretical decision-making framework for physical surveillance games. In Game Theory for Security and Risk Management. Springer, 129--156.
[3]
Ali Alshawish, Korbinian Spielvogel, and Hermann De Meer. 2019. A Model-based Time-to-Compromise Estimator to Assess the Security Posture of Vulnerable Networks. In 2019 International Conference on Networked Systems (NetSys). IEEE.
[4]
BSI: Bundesamt für Sicherheit in der Informationstechnik. 2018. IT-Grundschutz-Kompendium (1st ed.). Bundesanzeiger Verlag.
[5]
Christian Fruhwirth and Tomi Mannisto. 2009. Improving CVSS-based vulnerability prioritization and response with context information. In Proceedings of the 2009 3rd international Symposium on Empirical Software Engineering and Measurement. IEEE Computer Society, 535--544.
[6]
Gabriele Gianini, Marco Cremonini, Andrea Rainini, Guido Lena Cota, and Leopold Ghemmogne Fossi. 2015. A game theoretic approach to vulnerability patching. In Information and Communication Technology Research (ICTRC), 2015 International Conference on. IEEE, 88--91.
[7]
Jin B Hong, Dong Seong Kim, and Abdelkrim Haqiq. 2014. What vulnerability do we need to patch first?. In 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks. IEEE, 684--689.
[8]
David John Leversage and Eric James Byres. 2008. Estimating a System's Mean Time-to-Compromise. IEEE Security & Privacy 6 (01 2008), 52--60.
[9]
Louai Maghrabi, Eckhard Pfluegel, Luluwah Al-Fagih, Roman Graf, Giuseppe Settanni, and Florian Skopik. 2017. Improved software vulnerability patching techniques using CVSS and game theory. In Cyber Security And Protection Of Digital Services (Cyber Security), 2017 International Conference on. IEEE, 1--6.
[10]
Peter Mell, Tiffany Bergeron, and David Henning. 2005. NIST Special Publication 800-40 - Creating a Patch and Vulnerability Management Program (2nd ed.). National Institute of Standards and Technology.
[11]
Stefan Rass, Sandra Koenig, and Stefan Schauer. 2016. Decisions with Uncertain Consequences A Total Ordering on Loss-Distributions. PloS one 11, 12 (2016), e0168583.
[12]
Murugiah Souppaya and Karen Scarfone. 2013. NIST Special Publication 800-40 - Guide to Enterprise Patch Management Technologies (3rd ed.). National Institute of Standards and Technology.
[13]
Yichi Zhang, Lingfeng Wang, Yingmeng Xiang, and Chee-Wooi Ten. 2015. Power System Reliability Evaluation With SCADA Cybersecurity Considerations. IEEE Transactions on Smart Grid 6, 4 (July 2015), 1707--1721.

Cited By

View all
  • (2022)Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power OutagesEnergies10.3390/en1506195815:6(1958)Online publication date: 8-Mar-2022
  • (2020)Dynamic Risk-Aware Patch Scheduling2020 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS48642.2020.9162225(1-9)Online publication date: Jun-2020
  • (2019)Risk mitigation in electric power systems: Where to start?Energy Informatics10.1186/s42162-019-0099-62:1Online publication date: 13-Nov-2019

Index Terms

  1. Risk-based Decision-Support for Vulnerability Remediation in Electric Power Networks

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems
      June 2019
      589 pages
      ISBN:9781450366717
      DOI:10.1145/3307772
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 15 June 2019

      Check for updates

      Author Tags

      1. Risk-based patch prioritization
      2. game-theoretical decisions

      Qualifiers

      • Poster
      • Research
      • Refereed limited

      Funding Sources

      • BayStMWi - Bavarian Ministry of Economic Affairs, Regional Development and Energy

      Conference

      e-Energy '19
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 160 of 446 submissions, 36%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)3
      • Downloads (Last 6 weeks)2
      Reflects downloads up to 02 Mar 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2022)Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power OutagesEnergies10.3390/en1506195815:6(1958)Online publication date: 8-Mar-2022
      • (2020)Dynamic Risk-Aware Patch Scheduling2020 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS48642.2020.9162225(1-9)Online publication date: Jun-2020
      • (2019)Risk mitigation in electric power systems: Where to start?Energy Informatics10.1186/s42162-019-0099-62:1Online publication date: 13-Nov-2019

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media