@inproceedings{10.1145/3529836.3529926,
author = {Contreras, Crystal and Dokic, Hristina and Huang, Zhen and Stan Raicu, Daniela and Furst, Jacob and Tchoua, Roselyne},
title = {{Multiclass Classification of Software Vulnerabilities with Deep Learning}},
year = {2023},
isbn = {978-1-4503-9841-1/23/02},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3587716.3587738},
doi = {10.1145/3587716.3587738},
abstract = {Detecting software vulnerabilities has been a challenge for decades. Many techniques have been developed to detect vulnerabilities by reporting whether a vulnerability exists in the code of software. But few of them have the capability to categorize the types of detected vulnerabilities, which is crucial for human developers or other tools to analyze and address vulnerabilities. In this paper, we present our work on identifying the types of vulnerabilities using deep learning. Our data consists of code slices parsed in a manner that captures the syntax and semantics of a vulnerability, sourced from prior work. We train deep neural networks on these features to perform multiclass classification of software vulnerabilities in the dataset. Our experiments show that our models can effectively identify the vulnerability classes of the vulnerable functions in our dataset.},
booktitle = {Proceedings of the 15th International Conference on Machine Learning and Computing},
numpages = {7},
keywords = {vulnerability classification, software and application security, machine learning, deep learning, neural networks},
location = {Zhuhai, China},
series = {ICMLC 2023}
}