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A Transfer Learning Approach to Unveil the Role of Windows Common Configuration Enumerations in IEC 62443 Compliance

Miguel Bicudo, Estevão Rabello, Daniel Menasché, Paulo Segal, Claudio Segal, Anton Kocheturov, Priyanjan Sharma

TL;DR

The paper addresses automating IEC 62443-3-3 compliance for Windows in industrial control systems by mapping Windows CCEs to SRs using transfer learning from SUSE Linux CCE–SR associations. It introduces an embeddings-based, distance-weighted pipeline with a power transformation and a top-K with threshold selection, plus manual verification, to produce a labeled Windows CCE–SR dataset. Key findings show Windows configurations concentrate on SRs such as 5.2 and 7.6, achieve high agreement with LLM-derived labels (~95.6%), and enable automation, traceability, and cross-platform insights. The approach supports integration into continuous compliance pipelines and cross-framework alignment with standards like NIST SP 800-82 and ISO/IEC 27001, with reproducibility ensured through shared data and tooling, while highlighting the need for semantic adaptation across platforms due to domain differences.

Abstract

Industrial control systems (ICS) depend on highly heterogeneous environments where Linux, proprietary real-time operating systems, and Windows coexist. Although the IEC 62443-3-3 standard provides a comprehensive framework for securing such systems, translating its requirements into concrete configuration checks remains challenging, especially for Windows platforms. In this paper, we propose a transfer learning methodology that maps Windows Common Configuration Enumerations (CCEs) to IEC 62443-3-3 System Security Requirements by leveraging labeled Linux datasets. The resulting labeled dataset enables automated compliance checks, analysis of requirement prevalence, and identification of cross-platform similarities and divergences. Our results highlight the role of CCEs as a bridge between abstract standards and concrete configurations, advancing automation, traceability, and clarity in IEC 62443-3-3 compliance for Windows environments.

A Transfer Learning Approach to Unveil the Role of Windows Common Configuration Enumerations in IEC 62443 Compliance

TL;DR

The paper addresses automating IEC 62443-3-3 compliance for Windows in industrial control systems by mapping Windows CCEs to SRs using transfer learning from SUSE Linux CCE–SR associations. It introduces an embeddings-based, distance-weighted pipeline with a power transformation and a top-K with threshold selection, plus manual verification, to produce a labeled Windows CCE–SR dataset. Key findings show Windows configurations concentrate on SRs such as 5.2 and 7.6, achieve high agreement with LLM-derived labels (~95.6%), and enable automation, traceability, and cross-platform insights. The approach supports integration into continuous compliance pipelines and cross-framework alignment with standards like NIST SP 800-82 and ISO/IEC 27001, with reproducibility ensured through shared data and tooling, while highlighting the need for semantic adaptation across platforms due to domain differences.

Abstract

Industrial control systems (ICS) depend on highly heterogeneous environments where Linux, proprietary real-time operating systems, and Windows coexist. Although the IEC 62443-3-3 standard provides a comprehensive framework for securing such systems, translating its requirements into concrete configuration checks remains challenging, especially for Windows platforms. In this paper, we propose a transfer learning methodology that maps Windows Common Configuration Enumerations (CCEs) to IEC 62443-3-3 System Security Requirements by leveraging labeled Linux datasets. The resulting labeled dataset enables automated compliance checks, analysis of requirement prevalence, and identification of cross-platform similarities and divergences. Our results highlight the role of CCEs as a bridge between abstract standards and concrete configurations, advancing automation, traceability, and clarity in IEC 62443-3-3 compliance for Windows environments.
Paper Structure (12 sections, 3 equations, 6 figures, 3 tables)

This paper contains 12 sections, 3 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Initial Findings
  3. Methodology
  4. Learning IEC 62443-3-3 SRs
  5. Embeddings, Distance Metric and Weighting
  6. Enhancing variability
  7. Selection rule (top-$K$ with threshold).
  8. Average list size.
  9. Parameter tuning and manual verification.
  10. Validation
  11. Insights from Learned Mappings
  12. Conclusion

Figures (6)

  • Figure 1: Pipeline: Windows CCEs mapped to IEC 62443.
  • Figure 2: Counts of SRs and SUSE Linux configurations
  • Figure 3: Co-occurrence matrix of SUSE Linux System Security Requirements (SRs). We account for SUSE 15 DISA STIG. Each block corresponds to a set of SRs that tend to co-occur together.
  • Figure 4: Diversity index $M(p)$ as a function of $p$.
  • Figure 5: (a) Confusion-matrix heatmap; (b) Disagreement rates for SRs with the highest divergence.
  • ...and 1 more figures