CyBOKClaw: Human-in-the-Loop CyBOK Mapping for Cybersecurity Curriculum

Kevin Togbe; Yan Lin Aung

arxiv: 2605.24663 · v1 · pith:ZEHFLSNKnew · submitted 2026-05-23 · 💻 cs.CR · cs.AI

CyBOKClaw: Human-in-the-Loop CyBOK Mapping for Cybersecurity Curriculum

Yan Lin Aung , Kevin Togbe This is my paper

Pith reviewed 2026-06-30 12:51 UTC · model grok-4.3

classification 💻 cs.CR cs.AI

keywords CyBOKcybersecurity curriculumknowledge mappingretrieval frameworkhuman-in-the-looptop-k evaluationeducational keywordsexpert support system

0 comments

The pith

CyBOKClaw produces top-5 CyBOK candidates that experts accept as exact or closest match 98 percent of the time on validation data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents CyBOKClaw as a retrieval framework that maps cybersecurity keywords or phrases to the Cyber Security Body of Knowledge by generating short lists of candidates for expert review instead of demanding a single exact classification. It applies query normalization, curated term expansion, concept-level boosts, topic-description enrichment, and domain-sensitive ranking to handle the broad and ambiguous nature of educational terms. Performance is measured with structural metrics plus the ECA-5 score, which checks whether at least one top-5 candidate is an exact match or the nearest practical placement according to expert judgment. The system records 91.88 percent ECA-5 on development data and 98.00 percent on validation data, indicating that top-k expert usefulness gives a better picture of real utility than exact matching alone.

Core claim

CyBOKClaw is an interpretable human-in-the-loop retrieval framework that combines query normalization, curated term expansion, concept-level boosts, topic-description enrichment, and domain-sensitive ranking rules to produce top-k candidate mappings from cybersecurity keywords or phrases to CyBOK. On the development dataset it reaches 64.73 percent EXA-5, 84.18 percent structural semantic alignment, and 91.88 percent ECA-5; on the validation dataset the figures are 81.19 percent, 93.32 percent, and 98.00 percent. These results show that expert-guided top-k usefulness provides a more faithful account of practical CyBOK mapping utility than exact structural matching alone.

What carries the argument

The top-k candidate generator that integrates normalization, term expansion, boosts, enrichment, and ranking rules to support expert review of CyBOK mappings.

If this is right

Expert review of a short candidate list better captures practical utility for CyBOK mapping than requiring an exact structural match.
The combination of normalization, expansion, and ranking rules produces candidate sets that experts accept at high rates.
Performance on the expert usefulness metric rises from development to validation data, indicating the approach generalizes to new terms.
Structural semantic alignment alone understates the framework's value for curriculum tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Curriculum designers could embed the candidate generator in workflow tools to shorten the time spent on manual alignment.
The same human-in-the-loop pattern might transfer to mapping tasks for other domain bodies of knowledge.
If larger labeled sets become available, the ranking rules could be tuned further while preserving interpretability for experts.

Load-bearing premise

The ECA-5 metric based on expert judgment of top-5 candidates provides a faithful account of practical utility and the development and validation datasets represent real-world educational KWoPs.

What would settle it

A fresh collection of KWoPs drawn from actual cybersecurity courses in which experts judge that none of the top-5 candidates is an exact match or acceptable nearest placement for more than 10 percent of the terms.

Figures

Figures reproduced from arXiv: 2605.24663 by Kevin Togbe, Yan Lin Aung.

**Figure 1.** Figure 1: Overview of the CyBOKClaw. A cybersecurity keyword or phrase is first normalized and semantically enriched, then ranked against a flattened CyBOK Knowledge Tree using a combination of heuristic lexical, semantic, and curated signals. The ranked results are subsequently filtered using diversification strategies to produce a top-k candidate list. An optional explain mode provides score components and confide… view at source ↗

**Figure 2.** Figure 2: Example of CyBOK mapping and credit allocation. Module-level mappings and associated credits are aggregated by CyBOK Knowledge Area to produce a curriculum-level coverage profile. A CyBOK Mapping for Certification In the United Kingdom (UK), the National Cyber Security Centre (NCSC) operates a certification scheme for cybersecurity degree apprenticeships, undergraduate programmes, integrated master’s degr… view at source ↗

**Figure 3.** Figure 3: Coverage across CyBOK broad categories. Note that AI for Security is not a CyBOK broad category but a topic guide, and is included here to illustrate the curriculum’s coverage of AI-related cybersecurity content. from the development set and three from the validation set [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗

read the original abstract

This paper presents CyBOKClaw, an interpretable human-in-the-loop retrieval framework for mapping cybersecurity keywords or phrases (KWoPs) to the Cyber Security Body of Knowledge (CyBOK). Rather than treating the task as strict exact classification, the framework is designed as a top-k candidate generator for expert review. It combines query normalization, curated term expansion, concept-level boosts, topic-description enrichment, and domain-sensitive ranking rules. Because educational KWoPs are often broad, ambiguous, and only approximately aligned with CyBOK terminology, strict exact matching provides only a partial account of practical utility. We therefore evaluate the framework using both structural retrieval metrics and an expert-guided top-5 usefulness metric, ECA-5 (Exact or Closest Acceptable Match at top-5), which records whether the returned candidates contain at least one mapping that an expert would judge exact or accept as the nearest practical CyBOK placement. On the development dataset, CyBOKClaw achieves 64.73% EXA-5 (Exact Match at top-5), 84.18% structural semantic alignment, and 91.88% ECA-5; on the validation dataset, it achieves 81.19% EXA-5, 93.32% structural semantic alignment, and 98.00% ECA-5. These results show that expert-guided top-k usefulness provides a more faithful account of practical CyBOK mapping utility than exact structural matching alone, and that CyBOKClaw is effective as a CyBOK-specific expert-support retrieval system.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CyBOKClaw puts together a practical top-k retrieval setup for CyBOK mapping and reports usable numbers, but the ECA-5 scores rest on expert judgments whose process and dataset details are not shown.

read the letter

The main thing here is a domain-specific retrieval system that combines query normalization, term expansion, concept boosts, and custom ranking to produce top-5 CyBOK candidates for human review. It treats the task as approximate alignment rather than exact classification, which fits how educational keywords actually work. The paper gives concrete results on two datasets—64.73% EXA-5 and 91.88% ECA-5 on development data, rising to 81.19% and 98% on validation—along with a structural semantic alignment score. That is more than many similar tools report.

The component choices are straightforward but tailored, and the decision to prioritize an expert usefulness metric over pure exact match is reasonable for curriculum work. The framework is presented as an expert-support tool rather than a fully automatic classifier, which keeps the claims grounded.

The soft spot is the ECA-5 metric itself. It records whether an expert accepts at least one of the top-5 outputs as exact or closest acceptable, yet the abstract and available details give no numbers on how many experts judged, what their agreement was, whether they were blinded, or how the KWoP sets were sampled and sized. Without that, the high scores could come from an easy collection or lenient review rather than from the retrieval steps adding real value. The circularity risk is real: the metric is built around judging the system's own outputs.

This is for people who maintain or design cybersecurity curricula and need help aligning content to CyBOK. A reader already working on domain-specific IR or education tools could pick up the ranking rules and metric idea. It is narrow but the application is clear.

I would send it to peer review. The system is concrete and the numbers are there; referees can ask for the missing expert and dataset details without starting from zero.

Referee Report

2 major / 1 minor

Summary. The paper presents CyBOKClaw, a human-in-the-loop retrieval framework for mapping cybersecurity keywords or phrases (KWoPs) to CyBOK topics. It uses query normalization, curated term expansion, concept boosts, topic enrichment, and domain-sensitive ranking to generate top-k candidates for expert review rather than exact classification. The framework is evaluated on development and validation datasets using EXA-5 (exact match at top-5), structural semantic alignment, and the expert-judged ECA-5 metric (whether at least one top-5 candidate is exact or closest acceptable), reporting 64.73%/84.18%/91.88% on development data and 81.19%/93.32%/98.00% on validation data. The central claim is that ECA-5 provides a more faithful account of practical utility than exact matching alone and that CyBOKClaw is effective as a CyBOK-specific expert-support system.

Significance. If the evaluation methodology is made rigorous, the work provides a concrete, interpretable retrieval approach tailored to the ambiguities of educational cybersecurity terminology, with explicit credit for reporting numerical results on two distinct datasets and for distinguishing exact-match from expert-acceptance metrics. This could support curriculum design tasks where strict classification is insufficient.

major comments (2)

[Abstract] Abstract (evaluation description): The ECA-5 scores (91.88% development, 98.00% validation) that support the effectiveness claim are defined directly in terms of expert judgment on the system's own top-5 outputs, yet the manuscript provides no information on the number of experts, inter-rater agreement, blinding, conflict-of-interest controls, or sampling procedure. This detail is load-bearing for the central claim that the system is practically useful.
[Abstract] Abstract (evaluation description): The development and validation datasets are used to report all performance numbers, but no description is given of their construction, size, provenance, or how the KWoPs were sampled or selected. Without this, it is impossible to assess whether the reported ECA-5 figures reflect representative real-world educational mappings or an easy test set.

minor comments (1)

[Abstract] The abstract introduces multiple acronyms (KWoP, CyBOK, ECA-5, EXA-5) without initial expansion; while common in the subfield, explicit definitions on first use would improve accessibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the evaluation methodology. We address each major comment below and will revise the manuscript to provide the requested details on expert evaluation and dataset construction.

read point-by-point responses

Referee: [Abstract] Abstract (evaluation description): The ECA-5 scores (91.88% development, 98.00% validation) that support the effectiveness claim are defined directly in terms of expert judgment on the system's own top-5 outputs, yet the manuscript provides no information on the number of experts, inter-rater agreement, blinding, conflict-of-interest controls, or sampling procedure. This detail is load-bearing for the central claim that the system is practically useful.

Authors: We agree that the manuscript should provide more detail on the expert evaluation process underlying ECA-5 to support the practical-utility claim. In the revised version we will add a dedicated subsection on the expert review protocol, specifying the number of experts, inter-rater agreement statistics, blinding procedures, conflict-of-interest controls, and the sampling procedure used for the judged KWoPs. revision: yes
Referee: [Abstract] Abstract (evaluation description): The development and validation datasets are used to report all performance numbers, but no description is given of their construction, size, provenance, or how the KWoPs were sampled or selected. Without this, it is impossible to assess whether the reported ECA-5 figures reflect representative real-world educational mappings or an easy test set.

Authors: We agree that the manuscript must describe dataset construction, size, provenance, and sampling to allow assessment of representativeness. The revised manuscript will include an expanded section detailing both the development and validation datasets, their sizes, sources, and the criteria used to sample or select the KWoPs. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation on held-out data with external expert judgments

full rationale

The paper reports performance of a retrieval system on explicitly separated development and validation datasets using both structural metrics (EXA-5, semantic alignment) and the ECA-5 human-judgment metric. ECA-5 is defined as an external expert assessment of whether top-5 outputs contain an acceptable mapping; this is standard relevance evaluation and does not reduce the effectiveness claim to a tautology or fitted input by construction. No equations, self-citations, or uniqueness theorems are invoked to derive the results. The central claim rests on reported numbers from independent validation data rather than any self-referential loop.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on standard retrieval methods plus domain-specific rules and expert evaluation; the central claim depends on the validity of those expert assessments as an external benchmark.

axioms (1)

domain assumption Expert judgment is a reliable and unbiased way to assess the usefulness of CyBOK mappings
ECA-5 is defined in terms of whether experts judge a candidate as exact or closest acceptable match.

pith-pipeline@v0.9.1-grok · 5821 in / 1236 out tokens · 52945 ms · 2026-06-30T12:51:10.133447+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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(eds.): TREC: Experiment and Evaluation in Information Retrieval

Voorhees, E.M., Harman, D.K. (eds.): TREC: Experiment and Evaluation in Information Retrieval. Digital Libraries and Electronic Publishing, MIT Press, Cambridge, MA (2005) Fig. 2.Example of CyBOK mapping and credit allocation. Module-level mappings and associated credits are aggregated by CyBOK Knowledge Area to produce a curriculum-level coverage profile...

2005