KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #cli #open-source #ai #claude-code #codex #developer-tools #react #windows #deepseek-harness #ai-agent

TransferRepresentationLearning

★ 35 Open GitHub ↗

Cross-Project Transfer Representation Learning for Vulnerable Function Discovery

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## Transferable Representation Learning

Hi there, welcome to this page!

This page contains the code and data used in the paper [Cross-Project Transfer Representation Learning for Vulnerable Function Discovery](https://ieeexplore.ieee.org/abstract/document/8329207/) by Guanjun Lin; Jun Zhang; Wei Luo; Lei Pan; Yang Xiang; Olivier De Vel and Paul Montague.

### Instructions:

The Vulnerabilities_info.xlsx file contains information of the collected function-level vulnerabilities (It just a record for reference.). These vulnerabilities are from 6 open source projects: [FFmpeg](https://github.com/FFmpeg/FFmpeg), [LibTIFF](https://github.com/vadz/libtiff), [LibPNG](https://github.com/glennrp/libpng), [Pidgin](https://pidgin.im/), [Asterisk](https://www.asterisk.org/get-started) and [VLC Media Player](https://www.videolan.org/vlc/index.html). And vulnerability information was collected from [National Vulnerability Database(NVD)](https://nvd.nist.gov/) until the end of July 2017.

### Requirements for code:

* [Tensorflow](https://www.tensorflow.org/)
 * [Keras](https://github.com/fchollet/keras/tree/master/keras)
 * [Scikit-learn](http://scikit-learn.org/stable/)
 * [Gensim](https://radimrehurek.com/gensim/)
 * Python >= 2.7

The dependencies can be installed using [Anaconda](https://www.anaconda.com/download/). For example:

```bash
$ bash Anaconda3-5.0.1-Linux-x86_64.sh
```

The "Data" folder contains the following sub folders:
1) VulnerabilityData -- It contains a ZIP file which stores the vulnerable and part of non_vulnerable functions from 6 open source projects. Unzip the file, one will find 6 folders named with the projects. Each folder contains the source code of the non-vulnerable functions (named with their function names) and vulnerable functions (named with the CVE IDs):
   * The vulnerable functions are all named with the CVE IDs (their names are starting with ‘cve-’ or ‘CVE-’). For example, “cve-2017-14005.c” is a vulnerable function. 
   * The non-vulnerable functions are named with the format: “xxxx_file_name_function_name.c” to avoid duplicated file/function names. For example “1374_cmdutils.c_show_devices.c” is a non-vulnerable function.

In the pre-training phase, one can choose any 5 projects as the historical data for training a LSTM network (the labels can be generated based on the file names (vulnerable functions have the CVE IDs as their file names. Please see the code for more details). Then, the remaining 1 project can be used as the input to the pre-trained network for generating representations. Finally, the generated representations can be used as features for training a classifier.

2) CodeMetrics -- It stores the code metrics extracted from the source code files of the open source projects. The code metrics are used as features to train a random forest classifier as the baseline to compare with the method which uses transfer-learned representations as features. We used [Understand](https://scitools.com/) which is a commercial code enhancement tool for extracting function-level code metrics. We included 23 code metrics extracted from the vulnerable functions of 6 projects.
 
3) TrainedTokenizer -- It contains the trained tokenizer file which is used for converting the serialized AST lists to numeric tokens.

4) TrainedWord2vecModel -- It includes the trained Word2vec model. The model was trained on the code base of 6 open source projects. The Word2vec model is used in the embedding layer of the LSTM network for converting input sequence to meaningful embeddings.

The "Code" folder contains the Python code samples. 
1) TransferableRepresentationLearning_LSTM_DNN.py file is for LSTM network training. It defines the structure of the Bi-LSTM network used in the paper. The input of the file is the historical vulnerable functions that have labels. The output of the file is a trained LSTM network capable of obtaining vulnerable function representations.

2) ExtractLearnedFeaturesAndClassification.py file is for obtaining the function representations from the pre-trained LSTM network. It also includes the code for training a random forest classifier based on the obtained function representations as features.

3) CodeMetrics.py file is to train a random forest classifier based on the selected 23 code metrics.

If you are interested in our project, please contact junzhang@swin.edu.au for more information. If you use our code and data in your work, please kindly cite our paper in your work.

The latex format:

```
@article{lin2018cross,
  title={Cross-Project Transfer Representation Learning for Vulnerable Function Discovery},
  author={Lin, Guanjun and Zhang, Jun and Luo, Wei and Pan, Lei and Xiang, Yang and De Vel, Olivier and Montague, Paul},
  journal={IEEE Transactions on Industrial Informatics},
  year={2018},
  publisher={IEEE}
}
```

Thank you!

TransferRepresentationLearning

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README