Sec B E R T

Introduction

SecBERT is a pretrained language model specifically designed for processing cybersecurity texts. It is based on the BERT architecture and trained on a variety of cybersecurity datasets. SecBERT aims to enhance performance in tasks such as Named Entity Recognition (NER), text classification, semantic understanding, and question answering within the cybersecurity domain.

Architecture

SecBERT follows the BERT architecture with modifications tailored for cybersecurity text. It includes a custom wordpiece vocabulary, called secvocab, optimized for the training corpus consisting of cybersecurity-specific literature and datasets.

Training

The model was trained using a corpus derived from multiple cybersecurity datasets:

• APTnotes
• Stucco-Data
• CASIE
• SemEval-2018 Task 8 (SecureNLP)

These datasets provide a comprehensive range of cybersecurity texts to ensure the model's proficiency in understanding domain-specific language and context.

Guide: Running Locally

To run SecBERT locally, follow these steps:

1. Install Dependencies: Ensure you have Python and PyTorch installed. You can install the Hugging Face Transformers library using pip:

   pip install transformers
   

2. Load the Model: Use the Transformers library to load SecBERT.

   from transformers import AutoModelForMaskedLM, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("jackaduma/SecBERT")
   model = AutoModelForMaskedLM.from_pretrained("jackaduma/SecBERT")
   

3. Run Inference: Use the model to perform inference on your cybersecurity text data.

For those needing additional computational power, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

SecBERT is licensed under the Apache License 2.0, allowing for both commercial and non-commercial use, modification, and distribution, provided that any redistributed software includes a copy of the license and any modifications are documented.