mstockert/SPEC5G

This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is under review.

SPEC5G

This repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" which is under review.

SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on ML-based natural language processing (NLP) tasks, we have used this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task.

SPEC5G is the first-ever public 5G dataset for NLP research on network security.

Updates

• The pretrained models are now available for download.

Table of Contents

• SPEC5G
  • Updates
  • Table of Contents
  • Datasets
  • Models
  • Dependencies
  • Training & Evaluation

Datasets

Download the dataset from here. This includes:

• Our original 134M Word training corpus (Gold_5G_v4.0.zip)
• 5GSum - Summarization Dataset (simplification_dataset.csv)
• 5GSC - Classification Dataset (5GSC.csv)

Models

The pretrained model checkpoints can be found below:

• BERT5G
• RoBERTa5G
• XLNET5G

Dependencies

• Python 3.7+
• beautifulsoup4
• textract
• selenium
• datasets
• tokenizers
• transformers
• rouge_score
• PyTorch

• sentencepiece (Install CLI)
• regex
• torchtext (pip install torchtext==0.4.0)

Training & Evaluation

• See training and evaluation module.

Citation

If you use this dataset or code modules, please cite the following paper:

@misc{https://doi.org/10.48550/arxiv.2301.09201,
  doi = {10.48550/ARXIV.2301.09201},
  url = {https://arxiv.org/abs/2301.09201},
  author = {Karim, Imtiaz and Mubasshir, Kazi Samin and Rahman, Mirza Masfiqur and Bertino, Elisa},
  keywords = {Information Retrieval (cs.IR), Cryptography and Security (cs.CR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and       information sciences},
  title = {SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis},
  publisher = {arXiv},
  year = {2023},
  copyright = {Creative Commons Attribution 4.0 International}
}