tuandunghcmut/cti_bench_rcm_2021

CTI-Bench: Reverse Cyber Mapping (RCM) (2021)

Dataset Description

This dataset contains 1,000 examples for reverse cyber mapping tasks. It evaluates a model's ability to work backwards from observed indicators or effects to identify the underlying attack techniques, tools, or threat actors.

Dataset Structure

Each example contains:

• task_type: Always "reverse_cyber_mapping"
• Additional fields vary based on the specific mapping task
• Common fields include indicators, observables, and mapping targets

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("tuandunghcmut/cti_bench_rcm_2021")

# Access a sample
sample = dataset['train'][0]
print(f"Task: {sample['task_type']}")

Reverse Mapping Categories

The dataset may include mapping from:

• Indicators of Compromise (IoCs) → Attack techniques
• Network signatures → Malware families
• Attack patterns → Threat actors
• Behavioral analysis → MITRE techniques

Citation

@article{ctibench2024,
  title={CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence},
  author={[Authors]},
  journal={NeurIPS 2024},
  year={2024}
}

Original Source

This dataset is derived from CTI-Bench and is available under the same license terms.

Tasks

This dataset is designed for:

• ✅ Reverse engineering of attack chains
• ✅ Indicator-to-technique mapping
• ✅ Threat hunting and investigation
• ✅ Forensic analysis