CodeGrapher: An Image Representation Method to Enhance Software Vulnerability Prediction

Ramin Fuladi, Khadija Hanifi

2024

Abstract

Contemporary software systems face a severe threat from vulnerabilities, prompting exploration of innovative solutions. Machine Learning (ML) algorithms have emerged as promising tools for predicting software vulnerabilities. However, the diverse sizes of source codes pose a significant obstacle, resulting in varied numerical vector sizes. This diversity disrupts the uniformity needed for ML models, causing information loss, increased false positives, and false negatives, diminishing vulnerability analysis accuracy. In response, we propose CodeGrapher, preserving semantic relations within source code during vulnerability prediction. Our approach involves converting numerical vector representations into image sets for ML input, incorporating similarity distance metrics to maintain vital code relationships. Using Abstract Syntax Tree (AST) representation and skip-gram embedding for numerical vector conversion, CodeGrapher demonstrates potential to significantly enhance prediction accuracy. Leveraging image scalability and resizability addresses challenges from varying numerical vector sizes in ML-based vulnerability prediction. By converting input vectors to images with a set size, CodeGrapher preserves semantic relations, promising improved software security and resilient systems.

Paper Citation

in Harvard Style

Fuladi R. and Hanifi K. (2024). CodeGrapher: An Image Representation Method to Enhance Software Vulnerability Prediction. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-696-5, SciTePress, pages 666-673. DOI: 10.5220/0012717100003687

in Bibtex Style

@conference{enase24,
author={Ramin Fuladi and Khadija Hanifi},
title={CodeGrapher: An Image Representation Method to Enhance Software Vulnerability Prediction},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2024},
pages={666-673},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012717100003687},
isbn={978-989-758-696-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - CodeGrapher: An Image Representation Method to Enhance Software Vulnerability Prediction
SN - 978-989-758-696-5
AU - Fuladi R.
AU - Hanifi K.
PY - 2024
SP - 666
EP - 673
DO - 10.5220/0012717100003687
PB - SciTePress