Sec Ro B E R Ta

Introduction

SecRoBERTa is a pre-trained language model specifically designed for processing cybersecurity-related text. It builds on the RoBERTa architecture and is trained to handle various cybersecurity tasks such as Named Entity Recognition (NER), Text Classification, Semantic Understanding, and Question Answering (Q&A).

Architecture

SecRoBERTa leverages a wordpiece vocabulary (secvocab) tailored to the unique characteristics of cybersecurity text. The model is an extension of the RoBERTa framework, optimized for cybersecurity applications.

Training

The model was trained using a corpus derived from several cybersecurity sources:

• APTnotes: A collection of reports on Advanced Persistent Threats.
• Stucco-Data: A database of cybersecurity data sources.
• CASIE: A dataset for extracting cybersecurity event information.
• SemEval-2018 Task 8: A competition dataset focused on semantic extraction from cybersecurity reports.

The training process involved optimizing the model to improve performance on downstream cybersecurity tasks.

Guide: Running Locally

To run SecRoBERTa locally, follow these steps:

1. Clone the Repository: Obtain the code from the SecBERT repository.
2. Set Up Environment: Install the necessary dependencies, including PyTorch and Hugging Face Transformers.
3. Download Model: Use the Hugging Face API to load SecRoBERTa from the Hugging Face model hub.
4. Run Inference: Utilize the model for fill-mask tasks or fine-tune it for specific applications using your own data.

For enhanced performance, consider using cloud-based GPUs from providers like AWS, Google Cloud, or Azure.

License

SecRoBERTa is released under the Apache-2.0 License, allowing for free use, modification, and distribution, provided that conditions of the license are met.