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Empowering Vulnerability Prioritization: A Heterogeneous Graph-Driven Framework for Exploitability Prediction

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Web Information Systems Engineering – WISE 2023 (WISE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14306))

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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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Notes

  1. 1.

    https://nvd.nist.gov/.

  2. 2.

    https://www.exploit-db.com/.

  3. 3.

    https://www.cvedetails.com/.

  4. 4.

    https://cwe.mitre.org/.

  5. 5.

    https://www.exploit-db.com/.

  6. 6.

    https://neo4j.com/docs/graph-data-science/current/algorithms/.

  7. 7.

    https://scikit-learn.org/stable/.

  8. 8.

    https://neo4j.com/.

  9. 9.

    https://pytorch-geometric.readthedocs.io/en/latest/index.html.

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Author information

Authors and Affiliations

  1. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia

    Jiao Yin, Wei Hong, Hua Wang & Yuan Miao

  2. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, Guangdong, China

    Guihong Chen

  3. School of Cyber Security, Guangdong Polytechnic Normal University, Guangzhou, 510000, Guangdong, China

    Guihong Chen

  4. Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, 3086, Australia

    Jinli Cao

Authors
  1. Jiao Yin
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  2. Guihong Chen
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  3. Wei Hong
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  4. Hua Wang
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  5. Jinli Cao
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  6. Yuan Miao
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Corresponding author

Correspondence to Jiao Yin .

Editor information

Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Feng Zhang

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. Qatar University, Doha, Qatar

    Mahmoud Barhamgi

  4. Swinburne University of Technology, Hawthorn, Australia

    Lu Chen

  5. Swinburne University of Technology, Hawthorn, Australia

    Rui Zhou

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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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  • DOI: https://doi.org/10.1007/978-981-99-7254-8_23

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