Abstract
With the increasing number of software vulnerabilities being disclosed each year, prioritizing them becomes essential as it is challenging to patch all of them promptly. Exploitability prediction plays a crucial role in assessing the severity of vulnerabilities and determining their prioritization. Most existing works on exploitability prediction focus on building predictive models based on features extracted from individual vulnerabilities, neglecting the relationships between vulnerabilities and their contextual information. Only a few studies have explored using homogeneous graph-based techniques to enhance performance in this domain. This paper proposes a novel heterogeneous graph-driven framework for enhancing vulnerability exploitability prediction. The framework comprises two heterogeneous graph feature extraction technique streams: topological feature concatenation and node embedding based on heterogeneous graph neural networks (HGNN). Experimental results demonstrate that both streams, leveraging heterogeneous graph-based features, significantly improve the performance of exploitability prediction compared with using features extracted from individual vulnerabilities. Specifically, the two streams achieve 5.44% and 2.06% improvement in the F1 score, respectively. The data and codes are available on GitHub (https://github.com/happyResearcher/HG-VEP) to facilitate reproducibility and further research in this field.
The work reported in this paper was partly supported by the Australian Research Council (ARC) Linkage Project LP180101062.
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Yin, J., Chen, G., Hong, W., Wang, H., Cao, J., Miao, Y. (2023). Empowering Vulnerability Prioritization: A Heterogeneous Graph-Driven Framework for Exploitability Prediction. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_23
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