Update README.md

README.md

@@ -1,4 +1,19 @@
 ---
+license: other
+task_categories:
+- text-classification
+- question-answering
+- multiple-choice
+- text-generation
+tags:
+- cybersecurity
+- industrial-control-systems
+- ics
+- benchmark
+- llm-evaluation
+- mirror
+size_categories:
+- 1K<n<10K
 dataset_info:
 - config_name: CPST
   features:
@@ -153,4 +168,140 @@ configs:
     path: VOOD/test-*
   - split: val
     path: VOOD/val-*
+pretty_name: SECURE (Mirror)
 ---
+
+# Dataset Card for SECURE (RISys-Lab Mirror)
+
+> **⚠️ Disclaimer:** > This repository is a **mirror/re-host** of the original [SECURE benchmark](https://github.com/aiforsec/SECURE).  
+> **RISys-Lab is not the author of this dataset.** We are hosting this copy in Parquet format to ensure seamless integration and stability for our internal evaluation pipelines. All credit belongs to the original authors listed below.
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Repository Intent](#repository-intent)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks)
+- [Dataset Structure](#dataset-structure)
+  - [Data Splits & Configurations](#data-splits--configurations)
+  - [Data Fields](#data-fields)
+- [Usage](#usage)
+- [Additional Information](#additional-information)
+  - [Original Authors](#original-authors)
+  - [Citation](#citation)
+  - [License](#license)
+
+## Dataset Description
+
+- **Original Repository:** [https://github.com/aiforsec/SECURE](https://github.com/aiforsec/SECURE)
+- **Original Paper:** [SECURE: Benchmarking Large Language Models for Cybersecurity Advisory (ArXiv 2405.20441)](https://arxiv.org/abs/2405.20441)
+- **Mirror Maintainer:** RISys-Lab (for evaluation pipeline compatibility)
+
+### Repository Intent
+This Hugging Face dataset is a re-host of the original **SECURE** benchmark. It has been converted to **Parquet format** to support efficient loading and configuration handling in the `datasets` library. If you are looking for the official release, please visit the [Original GitHub Repository](https://github.com/aiforsec/SECURE).
+
+### Dataset Summary
+**SECURE** (Security Extraction, Understanding & Reasoning Evaluation) is a benchmark designed to assess Large Language Models (LLMs) in realistic cybersecurity scenarios, with a specific focus on **Industrial Control Systems (ICS)**.
+
+The benchmark consists of six distinct datasets that evaluate knowledge extraction, understanding, and reasoning based on industry-standard sources (such as CISA advisories and MITRE frameworks).
+
+### Supported Tasks
+The dataset is divided into six configurations, each targeting a specific skill:
+
+1.  **MAET (MITRE ATT&CK Extraction Task):** Multiple-choice questions mapping attack behaviors to MITRE ATT&CK techniques.
+2.  **CWET (Common Weakness Extraction Task):** Multiple-choice questions identifying Common Weaknesses (CWEs) from descriptions.
+3.  **KCV (Knowledge test on Common Vulnerabilities):** A knowledge verification task for CVEs.
+4.  **VOOD (Vulnerability Out-of-Distribution):** A task designed to test model performance on out-of-distribution vulnerability data.
+5.  **RERT (Risk Evaluation Reasoning Task):** Evaluating the model's ability to reason about risk based on vulnerability overviews (e.g., from CISA ICS advisories).
+6.  **CPST (CVSS Problem Solving Task):** A regression/reasoning task where the model must determine the CVSS (Common Vulnerability Scoring System) score.
+
+## Dataset Structure
+
+### Data Splits & Configurations
+The dataset is organized into 6 configurations. 
+
+> **Important Note on Validation Splits:** > The original SECURE benchmark provided test sets. To facilitate **few-shot evaluation** in our pipeline, we randomly sampled 5 examples from the original data to create a `val` split for each configuration.
+
+| Config Name | Full Task Name | Validation Size (Few-Shot) | Test Size |
+| :--- | :--- | :--- | :--- |
+| `MAET` | MITRE ATT&CK Extraction Task | 5 | 1,067 |
+| `CWET` | Common Weakness Extraction Task | 5 | 959 |
+| `KCV` | Knowledge on Common Vulnerabilities | 5 | 461 |
+| `VOOD` | Vulnerability Out-of-Distribution | 5 | 461 |
+| `RERT` | Risk Evaluation Reasoning Task | 5 | 995 |
+| `CPST` | CVSS Problem Solving Task | 5 | 95 |
+
+### Data Fields
+
+#### `MAET`, `CWET` (Multiple Choice)
+- `url` (string): Source URL.
+- `prompt` (string): The full input prompt.
+- `question` (string): The specific question text.
+- `options` (sequence): A list/sequence of answer choices (e.g., `["Option A...", "Option B..."]`).
+- `answer` (string): The correct option (e.g., "A").
+
+#### `KCV`, `VOOD` (True / False)
+- `url` (string): Source URL.
+- `prompt` (string): The full input prompt.
+- `question` (string): The question text.
+- `answer` (string): The correct answer string.
+
+#### `RERT` (Reasoning)
+- `source-url` (string): Source URL (e.g., CISA advisory).
+- `prompt` (string): The input prompt requesting a risk evaluation.
+- `vulnerability-overview` (string): Context describing the vulnerability.
+- `answer` (string): The gold-standard risk evaluation text.
+
+#### `CPST` (Scoring)
+- `prompt` (string): The input prompt containing vulnerability details.
+- `cvss-v3-vector-string` (string): The CVSS vector string (e.g., `CVSS:3.1/AV:N/AC:L...`).
+- `answer` (float64): The correct CVSS Base Score (e.g., `7.5`).
+
+## Usage
+
+You can load a specific task using the configuration name.
+
+```python
+from datasets import load_dataset
+
+# Load the MITRE ATT&CK (MAET) test set
+dataset = load_dataset("RISys-Lab/Benchmarks_CyberSec_SECURE", "MAET", split="test")
+
+# Load the few-shot examples (val split)
+few_shot_examples = load_dataset("RISys-Lab/Benchmarks_CyberSec_SECURE", "MAET", split="val")
+
+# Access an example
+print(dataset[0])
+
+```
+
+## Additional Information
+
+### Original Authors
+
+The dataset was developed by:
+
+* **Dipkamal Bhusal**
+* **Nidhi Rastogi**
+* **Md Tanvirul Alam**
+* *(and contributors from Rochester Institute of Technology)*
+
+### Citation
+
+Please cite the original ArXiv paper if you use this dataset:
+
+```bibtex
+@misc{bhusal2024securebenchmarkinglargelanguage,
+      title={SECURE: Benchmarking Large Language Models for Cybersecurity Advisory}, 
+      author={Dipkamal Bhusal and Nidhi Rastogi and Md Tanvirul Alam and Le Nguyen and Xashru Shrestha and Qiben Yan and Rui Li and Tuan Vu and Nathan Lewis and Y. S. Rao},
+      year={2024},
+      eprint={2405.20441},
+      archivePrefix={arXiv},
+      primaryClass={cs.CR},
+      url={https://arxiv.org/abs/2405.20441}, 
+}
+
+```
+
+### License
+
+An explicit license file was not found in the original repository. This mirror is provided for research purposes. All rights remain with the original authors.