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Reducing Information Overload: Because Even Security Experts Need to Blink

Philipp Kuehn, Markus Bayer, Tobias Frey, Moritz Kerk, Christian Reuter

TL;DR

The paper addresses information overload in CERT operations by evaluating 196 embedding–clustering configurations across five security datasets to identify practical automated consolidation methods that retain semantic coherence. It introduces ThreatReport as a labeled corpus and conducts a broad, cross-dataset comparison of 14 clustering algorithms and 14 embeddings, emphasizing external homogeneity metrics. Results show substantial potential to reduce manual review workload (often exceeding 90%) with end-to-end runtimes of a few minutes on consumer hardware, though performance is data- and parameter-dependent, requiring domain-specific tuning (notably for ThreatReport). The work provides actionable guidance for deploying clustering in CERT workflows and contributes an open-source framework to foster further optimization and domain-specific enhancements.

Abstract

Computer Emergency Response Teams (CERTs) face increasing challenges processing the growing volume of security-related information. Daily manual analysis of threat reports, security advisories, and vulnerability announcements leads to information overload, contributing to burnout and attrition among security professionals. This work evaluates 196 combinations of clustering algorithms and embedding models across five security-related datasets to identify optimal approaches for automated information consolidation. We demonstrate that clustering can reduce information processing requirements by over 90% while maintaining semantic coherence, with deep clustering achieving homogeneity of 0.88 for security bug report (SBR) and partition-based clustering reaching 0.51 for advisory data. Our solution requires minimal configuration, preserves all data points, and processes new information within five minutes on consumer hardware. The findings suggest that clustering approaches can significantly enhance CERT operational efficiency, potentially saving over 3.750 work hours annually per analyst while maintaining analytical integrity. However, complex threat reports require careful parameter tuning to achieve acceptable performance, indicating areas for future optimization. The code is made available at https://github.com/PEASEC/reducing-information-overload.

Reducing Information Overload: Because Even Security Experts Need to Blink

TL;DR

The paper addresses information overload in CERT operations by evaluating 196 embedding–clustering configurations across five security datasets to identify practical automated consolidation methods that retain semantic coherence. It introduces ThreatReport as a labeled corpus and conducts a broad, cross-dataset comparison of 14 clustering algorithms and 14 embeddings, emphasizing external homogeneity metrics. Results show substantial potential to reduce manual review workload (often exceeding 90%) with end-to-end runtimes of a few minutes on consumer hardware, though performance is data- and parameter-dependent, requiring domain-specific tuning (notably for ThreatReport). The work provides actionable guidance for deploying clustering in CERT workflows and contributes an open-source framework to foster further optimization and domain-specific enhancements.

Abstract

Computer Emergency Response Teams (CERTs) face increasing challenges processing the growing volume of security-related information. Daily manual analysis of threat reports, security advisories, and vulnerability announcements leads to information overload, contributing to burnout and attrition among security professionals. This work evaluates 196 combinations of clustering algorithms and embedding models across five security-related datasets to identify optimal approaches for automated information consolidation. We demonstrate that clustering can reduce information processing requirements by over 90% while maintaining semantic coherence, with deep clustering achieving homogeneity of 0.88 for security bug report (SBR) and partition-based clustering reaching 0.51 for advisory data. Our solution requires minimal configuration, preserves all data points, and processes new information within five minutes on consumer hardware. The findings suggest that clustering approaches can significantly enhance CERT operational efficiency, potentially saving over 3.750 work hours annually per analyst while maintaining analytical integrity. However, complex threat reports require careful parameter tuning to achieve acceptable performance, indicating areas for future optimization. The code is made available at https://github.com/PEASEC/reducing-information-overload.
Paper Structure (20 sections, 2 figures, 6 tables)

This paper contains 20 sections, 2 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Goal
  3. Contributions
  4. Outline
  5. Related Work
  6. Embeddings
  7. Clustering
  8. Evaluation
  9. Research Gap
  10. Methodology
  11. Text Corpora
  12. Operational Requirements for Automated *cert Information Processing
  13. Embeddings, Clustering, and Evaluation
  14. Evaluation
  15. Clustering Performance
  16. ...and 5 more sections

Figures (2)

  • Figure 1: Example texts from the different evaluation datasets (CySecAlert, MSE, ThreatReport, *sbr, and SMS).
  • Figure 2: Result of the best and worst performing security related datasets with regard to the mean homogeneity over 5.0 consecutive runs. Columns show the clustering algorithms and rows the used embeddings. The separated column and row depict the mean over each column and row, respectively. The models and algorithms are sorted by the rows/columns sum (descending), such that the top left shows the highest result, while the bottom right shows the worst.