README.md

---
license: mit
arxiv: 2205.12424
datasets:
- code_x_glue_cc_defect_detection
metrics:
- accuracy
- precision
- recall
- f1
- roc_auc
model-index:
  - name: VulBERTa MLP
    results:
      - task:
          type: defect-detection
        dataset:
          name: codexglue-devign
          type: codexglue-devign
        metrics:
          - name: Accuracy
            type: Accuracy
            value: 64.71
          - name: Precision
            type: Precision
            value: 64.80
          - name: Recall
            type: Recall
            value: 50.76
          - name: F1
            type: F1
            value: 56.93
          - name: ROC-AUC
            type: ROC-AUC
            value: 71.02
pipeline_tag: text-classification
tags:
- devign
- defect detection
- code
---

# VulBERTa MLP Devign
## VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection

![VulBERTa architecture](https://raw.githubusercontent.com/ICL-ml4csec/VulBERTa/main/VB.png)

## Overview
This model is the unofficial HuggingFace version of "[VulBERTa](https://github.com/ICL-ml4csec/VulBERTa/tree/main)" with an MLP classification head, trained on CodeXGlue Devign (C code), by Hazim Hanif & Sergio Maffeis (Imperial College London). I simplified the tokenization process by adding the cleaning (comment removal) step to the tokenizer and added the simplified tokenizer to this model repo as an AutoClass.

> This paper presents presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ projects. The model learns a deep knowledge representation of the code syntax and semantics, which we leverage to train vulnerability detection classifiers. We evaluate our approach on binary and multi-class vulnerability detection tasks across several datasets (Vuldeepecker, Draper, REVEAL and muVuldeepecker) and benchmarks (CodeXGLUE and D2A). The evaluation results show that VulBERTa achieves state-of-the-art performance and outperforms existing approaches across different datasets, despite its conceptual simplicity, and limited cost in terms of size of training data and number of model parameters.

## Usage
**You must install libclang for tokenization.**

```bash
pip install libclang
```

Note that due to the custom tokenizer, you must pass `trust_remote_code=True` when instantiating the model.
Example:
```
from transformers import pipeline
pipe = pipeline("text-classification", model="claudios/VulBERTa-MLP-Devign", trust_remote_code=True, return_all_scores=True)
pipe("static void filter_mirror_setup(NetFilterState *nf, Error **errp)\n{\n    MirrorState *s = FILTER_MIRROR(nf);\n    Chardev *chr;\n    chr = qemu_chr_find(s->outdev);\n    if (chr == NULL) {\n        error_set(errp, ERROR_CLASS_DEVICE_NOT_FOUND,\n                  \"Device '%s' not found\", s->outdev);\n    qemu_chr_fe_init(&s->chr_out, chr, errp);")
>> [[{'label': 'LABEL_0', 'score': 0.014685827307403088},
  {'label': 'LABEL_1', 'score': 0.985314130783081}]]
```

***

## Data
We provide all data required by VulBERTa.  
This includes:
 - Tokenizer training data
 - Pre-training data
 - Fine-tuning data

Please refer to the [data](https://github.com/ICL-ml4csec/VulBERTa/tree/main/data "data") directory for further instructions and details.

## Models
We provide all models pre-trained and fine-tuned by VulBERTa.  
This includes:
 - Trained tokenisers
 - Pre-trained VulBERTa model (core representation knowledge)
 - Fine-tuned VulBERTa-MLP and VulBERTa-CNN models

Please refer to the [models](https://github.com/ICL-ml4csec/VulBERTa/tree/main/models "models") directory for further instructions and details.

## How to use

In our project, we uses Jupyterlab notebook to run experiments.  
Therefore, we separate each task into different notebook:

 - [Pretraining_VulBERTa.ipynb](https://github.com/ICL-ml4csec/VulBERTa/blob/main/Pretraining_VulBERTa.ipynb "Pretraining_VulBERTa.ipynb") - Pre-trains the core VulBERTa knowledge representation model using DrapGH dataset.
 - [Finetuning_VulBERTa-MLP.ipynb](https://github.com/ICL-ml4csec/VulBERTa/blob/main/Finetuning_VulBERTa-MLP.ipynb "Finetuning_VulBERTa-MLP.ipynb") - Fine-tunes the VulBERTa-MLP model on a specific vulnerability detection dataset.
 - [Evaluation_VulBERTa-MLP.ipynb](https://github.com/ICL-ml4csec/VulBERTa/blob/main/Evaluation_VulBERTa-MLP.ipynb "Evaluation_VulBERTa-MLP.ipynb") - Evaluates the fine-tuned VulBERTa-MLP models on testing set of a specific vulnerability detection dataset.
 - [Finetuning+evaluation_VulBERTa-CNN](https://github.com/ICL-ml4csec/VulBERTa/blob/main/Finetuning%2Bevaluation_VulBERTa-CNN.ipynb "Finetuning+evaluation_VulBERTa-CNN.ipynb") - Fine-tunes VulBERTa-CNN models and evaluates it on a testing set of a specific vulnerability detection dataset.


## Citation

Accepted as conference paper (oral presentation) at the International Joint Conference on Neural Networks (IJCNN) 2022.  
Link to paper: https://ieeexplore.ieee.org/document/9892280  


```bibtex
@INPROCEEDINGS{hanif2022vulberta,
  author={Hanif, Hazim and Maffeis, Sergio},
  booktitle={2022 International Joint Conference on Neural Networks (IJCNN)}, 
  title={VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection}, 
  year={2022},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/IJCNN55064.2022.9892280}
  
}
```