ProvG-Searcher: A Graph Representation Learning Approach for Efficient Provenance Graph Search-Reference-Cited by-同舟云学术

ProvG-Searcher: A Graph Representation Learning Approach for Efficient Provenance Graph Search

Published:2023-11-15 Issue: Volume: Page:
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Container-title:Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
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Author:

Altinisik Enes¹^ORCID,Deniz Fatih¹^ORCID,Sencar Hüsrev Taha¹^ORCID

Affiliation:

1. Qatar Computing Research Institute, HBKU, Doha, Qatar

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3576915.3623187

Reference85 articles.

1. Abdulellah Alsaheel, Yuhong Nan, Shiqing Ma, Le Yu, et al. 2021. ATLAS: A Sequence-based Learning Approach for Attack Investigation. In USENIX Security Symposium.

2. Ben Athiwaratkun and Andrew Gordon Wilson. 2018. Hierarchical density order embeddings. arXiv preprint arXiv:1804.09843 (2018).

3. MITRE ATT&CK. 2021. MITRE ATT&CK. https://attack.mitre.org. Accessed: February 28, 2023.

4. Jinheon Baek Minki Kang and Sung Ju Hwang. 2021. Accurate learning of graph representations with graph multiset pooling. arXiv preprint arXiv:2102.11533.

5. SimGNN

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