Dehydrator: Enhancing Provenance Graph Storage via Hierarchical Encoding and Sequence Generation

As the scope and impact of cyber threats have expanded, analysts utilize provenance graphs constructed from kernel logs to hunt for threats and investigate attacks. The high frequency of kernel events and the persistence of attacks pose challenges for the efficient storage of provenance graphs. Current approaches can be categorized into two types: pruning-based storage (e.g., LogGC, CPR, and NodeMerge) and encoding-based storage (e.g., DeepZip, SLEUTH, ELISE, and Leonard). However, none of these methods simultaneously satisfy the following three requirements: 1) lossless content, 2) storage efficiency, and 3) query support. To address this gap, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small>, an efficient provenance graph storage system that fulfills all these requirements. For the logs generated by auditing frameworks, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small> uses field mapping encoding to filter field-level redundancy, hierarchical encoding to filter structure-level redundancy, and finally learns a deep neural network to support batch querying. We have conducted evaluations on seven datasets totaling over one billion log entries. Experimental results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small> reduces the storage space by 84.55%. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small> is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.36\times $ </tex-math></inline-formula> more efficient than PostgreSQL, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.16\times $ </tex-math></inline-formula> than Neo4j, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.17\times $ </tex-math></inline-formula> than Leonard (the work most closely related to <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small>, published at Usenix Security’23).

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Publication Details

Journal
IEEE Transactions on Information Forensics and Security
Published
2025-01-01
DOI
https://doi.org/10.1109/tifs.2025.3592824
Citations
1
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
4.17

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article

Dehydrator: Enhancing Provenance Graph Storage via Hierarchical Encoding and Sequence Generation

Tiantian Zhu, Jie Ying, Tieming Chen, Mingqi Lv
1 citations
IEEE Transactions on Information Forensics and Security
Scientific Computing and Data Management
4.17
article

Dehydrator: Enhancing Provenance Graph Storage via Hierarchical Encoding and Sequence Generation

Tiantian Zhu, Jie Ying, Tieming Chen, Mingqi Lv
article en
1 citations

Abstract

As the scope and impact of cyber threats have expanded, analysts utilize provenance graphs constructed from kernel logs to hunt for threats and investigate attacks. The high frequency of kernel events and the persistence of attacks pose challenges for the efficient storage of provenance graphs. Current approaches can be categorized into two types: pruning-based storage (e.g., LogGC, CPR, and NodeMerge) and encoding-based storage (e.g., DeepZip, SLEUTH, ELISE, and Leonard). However, none of these methods simultaneously satisfy the following three requirements: 1) lossless content, 2) storage efficiency, and 3) query support. To address this gap, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small>, an efficient provenance graph storage system that fulfills all these requirements. For the logs generated by auditing frameworks, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small> uses field mapping encoding to filter field-level redundancy, hierarchical encoding to filter structure-level redundancy, and finally learns a deep neural network to support batch querying. We have conducted evaluations on seven datasets totaling over one billion log entries. Experimental results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small> reduces the storage space by 84.55%. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small> is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.36\times $ </tex-math></inline-formula> more efficient than PostgreSQL, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.16\times $ </tex-math></inline-formula> than Neo4j, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.17\times $ </tex-math></inline-formula> than Leonard (the work most closely related to <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dehydrator</small>, published at Usenix Security’23).

IEEE Transactions on Information Forensics and SecurityVol. 20
Zhejiang University of Science and Technology (CN), Zhejiang Lab (CN), Zhejiang University of Technology (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 5%
Scientific Computing and Data Management
4.17
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