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On the Efficiency of Sampling and Countermeasures to Critical-Infrastructure-Targeted Malware Campaigns

Published: 25 February 2016 Publication History

Abstract

Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (e.g., infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using an hierarchical topology typical of factory automation networks, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.

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      Published In

      cover image ACM SIGMETRICS Performance Evaluation Review
      ACM SIGMETRICS Performance Evaluation Review  Volume 43, Issue 4
      March 2016
      61 pages
      ISSN:0163-5999
      DOI:10.1145/2897356
      • Editor:
      • Nidhi Hegde
      Issue’s Table of Contents
      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.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 25 February 2016
      Published in SIGMETRICS Volume 43, Issue 4

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      • (2022)Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A ReviewDynamic Games and Applications10.1007/s13235-022-00428-0Online publication date: 14-Feb-2022
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      • (2020)Improving Predictability of User-Affecting Metrics to Support Anomaly Detection in Cloud ServicesIEEE Access10.1109/ACCESS.2020.30285718(198152-198167)Online publication date: 2020
      • (2020)Beyond Herd Immunity Against Strategic AttackersIEEE Access10.1109/ACCESS.2020.29836528(66365-66399)Online publication date: 2020
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