App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 1

Efficient threat hunting method based on local behavior perception of provenance graph

Zhu Di1,2
Tang Renzhi1,2
Zhu Jiale1,2
Guo Chun1,2
Shen Guowei1,2
1. College of Computer Science and Technology, Guizhou University, Guiyang Guizhou 550025, China
2. Provincial Key Laboratory of Software Engineering and Information Security, Guiyang Guizhou 550025, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0133

Abstract

Provenance graphs provide fine-grained representations of system behaviors for detecting Advanced Persistent Threats (APT) . Existing threat hunting methods face several challenges when operating on large-scale provenance graphs, including the explosive growth of candidate subgraphs, strong dependence on entity attributes, and high false positive rates. To address these issues, this paper proposes PG-LBPHunt, an efficient threat hunting method based on local behavior perception in provenance graphs. PG-LBPHunt adopts a behavior-semantics-preserving subgraph sampling and structural reduction strategy to compress provenance graph scale and reduce computational overhead. In addition, it introduces a behavior-aware graph similarity matching model to perform multidimensional joint representation learning and cross-graph similarity matching for candidate subgraphs. Experimental results demonstrate that, compared with existing methods, PG-LBPHunt reduces the number of candidate subgraphs by more than 49% and decreases the average candidate subgraph size by over 17%. Meanwhile, PG-LBPHunt achieves up to 98.81% accuracy and 98.8% F1-score on public datasets. The results indicate that PG-LBPHunt maintains accurate and robust threat hunting performance even when discrepancies exist between threat intelligence descriptions and actual attack behaviors.

Foundation Support

贵州省科技计划项目(黔科合基础-ZK[2023]重点001)
贵州省科技计划项目[黔科合支撑[2023]一般447]

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0133
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 1

Publish History

[2026-08-27] Accepted Paper

Cite This Article

朱迪, 唐仁治, 朱佳乐, 等. 基于溯源图局部行为感知的高效威胁狩猎方法 [J]. 计算机应用研究, 2027, 44 (1). (2026-09-14). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0133. (Zhu Di, Tang Renzhi, Zhu Jiale, et al. Efficient threat hunting method based on local behavior perception of provenance graph [J]. Application Research of Computers, 2027, 44 (1). (2026-09-14). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0133. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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