VulBERTa-MLP-MVD / README.md

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	---
	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}

	}
	```