CyberMetric-500: Composite Cybersecurity Benchmark

• CyberMetric-500 is a rigorously designed benchmark and composite metric framework that evaluates cybersecurity knowledge, agility, and cost-effectiveness across multiple technical lineages.
• It integrates three distinct methodologies—a stratified MCQ dataset for LLM evaluation, a 500-dimensional real-time cyber agility vector, and a normalized cost-benefit index—to offer comprehensive insights.
• Its robust evaluation protocols, quality assurance processes, and empirical findings provide actionable benchmarks for comparing LLM and expert cybersecurity performance and guiding defense strategies.

CyberMetric-500 is a rigorously designed benchmark and composite metric framework for evaluating cybersecurity knowledge, cyber agility, or overall cyber posture depending on context. It appears in three distinct technical lineages: (1) as a stratified multiple-choice question (MCQ) dataset for benchmarking LLMs in cybersecurity (Tihanyi et al., 2024), (2) as a 500-dimensional real-time cyber agility metric synthesized from dynamic transforms of classical security indicators (Mireles et al., 2019), and (3) as a normalized, unified cost-benefit analytic scoring index (0–500 scale) capturing defense efficacy and resource tradeoffs (Iannacone et al., 2019). Across these use cases, CyberMetric-500 operationalizes domain expertise, statistical rigor, and interpretability, providing a quantitative vehicle for systematizing cybersecurity knowledge assessment, defensive agility, and cost-effectiveness.

1. Multiple-Choice Q&A Benchmark: Dataset Composition and Design

CyberMetric-500, as instantiated in the CyberMetric suite (Tihanyi et al., 2024), is a 500-item, four-option MCQ dataset engineered to evaluate LLMs and (by extension) human experts’ proficiency in cybersecurity. Drawn as a stratified subset of the full 10,000-question CyberMetric corpus, it preserves proportional coverage across seven major cybersecurity subdomains:

Domain	% of Questions	Number of Questions
Penetration Testing / Ethical Hacking	10	50
Cryptography	15	75
Network Security / IoT Security	10	50
Information Security / Governance	15	75
Compliance / Disaster Recovery	15	75
Cloud Security / Identity Management	15	75
NIST Guidelines / RFC Documents	20	100

Each item presents four options (A–D), with a single correct answer per question. The question pool was generated via Retrieval-Augmented Generation (RAG), using over 580 publicly-accessible cybersecurity documents (NIST SPs, RFCs, textbooks, research publications) totaling more than 100,000 pages.

2. Data Collection, Generation, and Quality Assurance

The MCQ dataset was produced through a multi-stage, hybrid pipeline combining automated RAG, LLM-assisted postprocessing, and extensive human vetting:

• PDF documents were parsed and chunked (8,000-token segments).
• GPT-3.5 was prompted to generate multiple MCQs per segment.
• Falcon-180B performed initial grammar fixes and semantic filtering.
• Human non-security validators removed questions deemed off-topic or ungrammatical.
• Additional passes included T5-base grammar correction, Falcon context-check (to filter orphan or figure-dependent content), and GPT-4-based confidence scanning to flag likely incorrect answers for further human review.

Following over-generation (11,000 items), systematic filtering and refinement (Falcon: 1.7%, humans: 2.3%, context/grammar/answer validation) yielded 10,560 high-quality questions. More than 200 total expert-hours were devoted to manual validation, ensuring that questions were relevant, accurate, context-complete, and appropriately referenced. Non-security questions, duplicate content, and any ambiguity in correctness or outdatedness were excluded.

3. Evaluation Protocol and Reporting Metrics

Evaluation on CyberMetric-500 adheres to a rigorous N=4 independent-run protocol per model, reporting:

• Mean accuracy (correct answers as a percentage of 500).
• Standard deviation ($\sigma$) across runs, with

$$\text{accuracy} = \frac{\text{correct answers}}{500} \times 100\%$$

$$\sigma = \sqrt{\frac{1}{N} \sum_{i=1}^N (x_i - \mu)^2}$$

where $x_i$ is the accuracy on run $i$, $\mu$ the mean accuracy, and $N=4$.

Evaluated models include proprietary (GPT-4, GPT-3.5, Gemini-pro) as well as open-source LLMs (Falcon-180B, Zephyr-7B). Human performance was only measured on the 80-question subset due to the impracticality of administering the full set at human scale.

4. Key Empirical Findings

Performance on CyberMetric-500 validates high discriminative power between models and preserves ranking stratification:

Model	Mean Accuracy	$\sigma$
GPT-4	94.30%	0.77%
GPT-3.5	87.30%	0.87%
Gemini-pro	85.05%	0.88%
Falcon-180B	77.80%	0.26%
Zephyr-7B	76.40%	0.00%

Human experts (on CyberMetric-80) averaged ≈72.2% (top: ≈88.8%), whereas smaller open models (e.g., Zephyr-7B) outperformed non-expert participants. Distinct model strengths included broad recall across NIST and historical standards; weaknesses included lagging adaptation to the most recent guideline updates (e.g., NIST SP 800-63B) and bitwise/mathematical computations that require external tools.

Key implications include the necessity for tool-augmented or more dynamically retrievable knowledge integration in LLMs, ongoing human-labeled and stratified benchmark development, and the potential for “Cyber” specialist wrapper architectures.

5. CyberMetric-500 as a 500-Dimensional Cyber Agility Metric

An orthogonal instantiation of CyberMetric-500 arises as a composite vector capturing cyber agility, based on the cyber agility metric framework (Mireles et al., 2019). From an initial pool $\mathcal{M} = \{M_1,\ldots,M_n\}$ of $n \approx 70$ normalized static security metrics (e.g., true/false positive rates, mean time to detect), each is mapped into seven dynamic dimensions:

• Generation-Time (GT)
• Effective-Generation-Time (EGT)
• Triggering-Time (TT)
• Lagging-Behind-Time (LBT)
• Evolutionary-Effectiveness (EE)
• Relative-Generational-Impact (RGI)
• Aggregated-Generational-Impact (AGI)

For each metric $M_i$, these dynamic transforms are computed over discrete defender/attacker generations $D_t, A_{t'}$. Redundant or correlated metrics are pruned (e.g., via principal component analysis or clustering), yielding a 500-dimensional feature vector $[m_1(t),...,m_{500}(t)]$, optionally weighted by expert judgment or statistical importance $w_k$:

$$\text{CyberMetric-500}(t)=\sum_{k=1}^{500} w_k\cdot m_k(t)$$

Pseudocode is provided for batch computation; normalization is enforced either through global min-max scaling or percentile clipping. Real-world validation is recommended (e.g., honeypot, CTF data) and robustness evaluated through sensitivity analysis. The resulting feature set functions as a dynamic dashboard for monitoring organizational cyber agility, blending both speed (“how fast?”) and efficacy (“how well?”) of defense evolution.

6. Unified Cost-Benefit Index: CyberMetric-500 as a Normalized Cybersecurity Score

A third formulation of CyberMetric-500 is as a normalized index (0–500 scale) integrating all direct defense, resource, labor, and attack costs into a single interpretable score (Iannacone et al., 2019). This model is as follows:

• Compute resource/labor costs:
  • Resource: $C_{I_R}$ (install), $C_{B_R}$ (baseline), $C_{T_R}$ (per-alert), $C_{IR_R}$ (incident response)
  • Labor: $C_{I_L}$ (deploy/configure), $C_{B_L}$ (maintenance), $C_{T_L}$ (alert triage), $C_{IR_L}$ (incident response)
• Model expected attack cost $C_\text{breach}(d) = b(1-e^{-\alpha d})$, where $b$ is max breach value, $\alpha$ models attack progression, and $d$ is time-to-detection.
• Calculate expected costs as functions of true/false positive rates, mean detection delays, and volume ($N_{\rm att}$, $N_{\rm events}$).

The index is calculated:

$$\text{CyberMetric-500} = 500 \times \frac{C_{\rm baseline} - C_{\rm total}}{C_{\rm baseline}}$$

where $C_{\rm baseline}$ is the total loss with no defense, and $C_{\rm total}$ includes all incurred costs over the period.

Step-by-step parameter selection, calibration, and example calculations are specified to ensure comparability and interpretability. The index enables apples-to-apples comparison of candidate architectures or policies, highlights cost-impactful inefficiencies, and is robust to typical operational deployment environments.

7. Limitations, Best Practices, and Applications

Across all implementations, CyberMetric-500’s effectiveness depends on high-fidelity, up-to-date input data (MCQ curation, metric logging, cost estimation), representative sampling, and rigorous evaluation protocols. Challenges include the need for granular, time-aligned logging in cyber agility measurement, careful normalization when metrics have unbounded ranges, the risk of dimensionality-induced overfitting, and parameter estimation consistency for cross-organizational scoring.

Best practices involve starting with reduced pilot metric sets, regular weight and parameter review, detailed audit logging for diagnostic traceability, and integrating qualitative intelligence alongside quantitative indices.

CyberMetric-500 serves as a reference standard for benchmarking both automated (LLM) and human cybersecurity expertise (Tihanyi et al., 2024), a robust, multidimensional indicator of defense agility (Mireles et al., 2019), and a scalable, decision-oriented performance index (Iannacone et al., 2019). Its open design and documentation support ongoing adaptation to evolving threat models and security technologies.