基于系统溯源图的威胁发现与取证分析综述

冷涛; 蔡利君; 于爱民; 朱子元; 马建刚; 李超飞; 牛瑞丞; 孟丹

doi:10.11959/j.issn.1000-436x.2022105

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基于系统溯源图的威胁发现与取证分析综述

综述 | 更新时间：2024-06-05

- 基于系统溯源图的威胁发现与取证分析综述
- Review of threat discovery and forensic analysis based on system provenance graph
- 通信学报 2022年43卷第7期页码：172-188
- 作者机构：
  
  1. 中国科学院信息工程研究所，北京 100093
  2. 中国科学院大学网络空间安全学院，北京 100049
  3. 四川警察学院智能警务四川省重点实验室，四川泸州 646000
- 作者简介：
  
  [ "冷涛（1986- ），男，四川合江人，中国科学院大学博士生，四川警察学院副教授，主要研究方向为APT攻击检测、取证分析" ]
  [ "蔡利君（1988- ），女，河南汝南人，博士，中国科学院信息工程研究所助理研究员，主要研究方向为攻击检测、内部威胁检测" ]
  [ "于爱民（1980- ），男，山西临汾人，博士，中国科学院信息工程研究所正高级工程师、博士生导师，主要研究方向为可信软件测评、基于大数据的行为异常检测" ]
  [ "朱子元（1980- ），男，河南汝州人，博士，中国科学院信息工程研究所研究员、博士生导师，主要研究方向为处理器安全技术、系统安全理论与技术等" ]
  [ "马建刚（1990- ），男，河北衡水人，中国科学院信息工程研究所高级工程师，主要研究方向为对抗网络高仿真、数据安全" ]
  [ "李超飞（1994- ），男，河南汝州人，中国科学院大学博士生，主要研究方向为加密流量、深度学习等" ]
  [ "牛瑞丞（1994- ），男，云南昆明人，中国科学院大学博士生，主要研究方向为恶意代码检测、深度学习等" ]
  [ "孟丹（1965- ），男，黑龙江哈尔滨人，博士，中国科学院信息工程研究所所长、研究员、博士生导师，主要研究方向为计算机系统安全、云计算安全等" ]
- 基金信息：
  
  中科院战略性先导科技专项基金资助项目(XDC02040200);智能警务四川省重点实验室资助项目(ZNJW2022ZZQN002)
- DOI：10.11959/j.issn.1000-436x.2022105
  中图分类号： TP391
- 网络首发：2022-06，
  
  纸质出版：2022-07-25
- 稿件说明：
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冷涛, 蔡利君, 于爱民, 等. 基于系统溯源图的威胁发现与取证分析综述[J]. 通信学报, 2022,43(7):172-188.

Tao LENG, Lijun CAI, Aimin YU, et al. Review of threat discovery and forensic analysis based on system provenance graph[J]. Journal on Communications, 2022, 43(7): 172-188.
冷涛, 蔡利君, 于爱民, 等. 基于系统溯源图的威胁发现与取证分析综述[J]. 通信学报, 2022,43(7):172-188. DOI： 10.11959/j.issn.1000-436x.2022105.

Tao LENG, Lijun CAI, Aimin YU, et al. Review of threat discovery and forensic analysis based on system provenance graph[J]. Journal on Communications, 2022, 43(7): 172-188. DOI： 10.11959/j.issn.1000-436x.2022105.

摘要

通过调研溯源图研究相关的文献，提出了基于系统溯源图的网络威胁发现和取证分析研究框架。详细综述了基于溯源图的数据采集、数据管理、数据查询和可视化方法；提出了基于规则、基于异常和基于学习的威胁检测分类方法；概括了基于威胁情报或基于战略、技术、过程驱动的威胁狩猎方法；总结了基于因果关系、序列学习、特殊领域语言查询和语义重建的取证分析方法；最后指出了未来的研究趋势。

Abstract

By investigating works of literature related to provenance graph research

a research framework for network threat discovery and forensic analysis based on system-level provenance graph was proposed.A detailed overview of data collection

data management

data query

and visualization methods based on provenance graphs was provided.The rule-based

anomaly-based

and learning-based threat detection classification methods were proposed.Threats based on threat intelligence or based on strategy

technology

and process-driven threats hunting methods were summarized.Forensic analysis methods based on causality

sequence learning

language query and semantic reconstruction in special fields were summarized.Finally

the future research trends were pointed out.

关键词

Keywords

references

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