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January 18, 2026Computers0 citationsOpen Access

A Business-Oriented Approach to Automated Threat Analysis for Large-Scale Infrastructure Systems

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COChiaki OtaharaHUHiroki UchiyamaMKMakoto Kayashima

Key Points

  • The research aims to streamline the threat analysis process in security design for large-scale infrastructure systems.
  • Systematization of past security design cases into reusable templates.
  • Development of an algorithm for automatic threat-analysis results generation.
  • Inclusion of business operations as an analytical asset to address overlaps in threat analysis.
  • Workload reduction by up to 84% compared to conventional manual analysis.
  • Coverage and accuracy of threat analysis maintained after automation.

Abstract

Security design for large-scale infrastructure systems requires substantial effort and often causes development delays. In line with NIST guidance, such systems should consider security design throughout a system development lifecycle. Nevertheless, performing security design in early phases of the lifecycle is difficult due to frequent specification changes and variability in analyst expertise, which causes repeated rework. The workload is particularly critical in threat analysis, the key activity of security design, because rework can inflate the workload. To address this challenge, we propose an automated threat-analysis method. Specifically, (i) we systematize past security design cases and develop “templates” that organize the system-configuration and security information required for threat analysis into a reusable 5W-based format (When, Where, Who, Why, What); (ii) we define dependencies among the templates and design an algorithm that automatically generates threat-analysis results; and (iii) observing that threat analysis of large-scale systems often yield overlaps, we introduce “business operations” as an analytical asset, which includes encompassing information, function, and physical resources. We apply our method to an actual large-scale operational system and confirm that it reduces the workload by up to 84% relative to conventional manual analysis, while maintaining both the coverage and the accuracy of the analysis.

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Cite This Study

Otahara et al. (2026) studied this question.

synapsesocial.com/papers/696c7835eb60fb80d139670chttps://doi.org/10.3390/computers15010066
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