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Improving the Effectiveness of Cyberdefense Measures

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Cyberdefense

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 342))

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Abstract

We present and illustrate a recursive model which automatically organizes relationships between indicators of compromise (IoC) into richer information sets. It combines insights from natural language processing, supervised clustering, and network analysis to identify relations between IoC and thus to reduce information fragmentation. The quality of this combined information improves with every IoC that is added to the network, so that defenders can generate a more comprehensive and complete picture about a threat almost in real time and at very low transaction cost.

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Notes

  1. 1.

    For example, a code syntax could require strings to be opened and closed by the brackets “{” and “}”, so that these reserved characters would have to be filtered.

  2. 2.

    Note that we disregard the feature IoC # in the subsequent analysis since it is collinear to timestamp.

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Author information

Authors and Affiliations

  1. Department of Defense Economics, Military Academy at the Swiss Federal Institute of Technology Zurich, Birmensdorf, Switzerland

    Sébastien Gillard & Cédric Aeschlimann

Authors
  1. Sébastien Gillard
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  2. Cédric Aeschlimann
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Corresponding author

Correspondence to Sébastien Gillard .

Editor information

Editors and Affiliations

  1. Department Head, Department of Defense Economics, Military Academy at the Swiss Federal Institute of Technology, Zurich, Switzerland

    Marcus Matthias Keupp

1 Electronic supplementary material

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Supplementary material 1 (zip 430 KB)

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Gillard, S., Aeschlimann, C. (2023). Improving the Effectiveness of Cyberdefense Measures. In: Keupp, M.M. (eds) Cyberdefense. International Series in Operations Research & Management Science, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-031-30191-9_14

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