Advances in Advanced Persistent Threat Detection and Provenance Research

doi:10.3969/j.issn.1671-1122.2026.06.001

Abstract

Abstract:

In recent years, advanced persistent threat (APT) attacks have experienced explosive growth, posing severe challenges to government agencies and critical infrastructure. Provenance-based intrusion detection systems (PIDS), which capture and analyze system-level dependency relationships, provide a key means for detecting complex and stealthy APT attacks. This paper first analyzed various benchmark datasets supporting PIDS research, highlighting their limitations in scale, complexity, attack coverage, and annotation quality. Furthermore, based on provenance graphs, it classified existing APT detection and investigation methods, revealing the technological evolution from early rule-based matching to machine learning and current approaches utilizing Large Language Model (LLM). The advantages and disadvantages of each category were summarized. This work presented systematic review of the paradigm for LLM-based APT detection and investigation and clarified the key challenges faced in this field. Finally, it outlined prospects for future research directions.

Key words: advanced persistent threat, provenance graph, intrusion detection system, large language model

CLC Number:

TP309

SUN Yu, ZHANG Xuanrui, LIU Xinyu. Advances in Advanced Persistent Threat Detection and Provenance Research[J]. Netinfo Security, 2026, 26(6): 833-853.

Figures/Tables 18

References 92

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维度	传统攻击	APT攻击
攻击者	个体或小组织网络犯罪分子	全球性、有组织、有纪律的黑客组织、敌对势力
攻击目标	随机性选择攻击，通常以个体为主，以达到获取金钱、盗窃身份、欺诈等目的	特定攻击目标，通常针对国家安全信息、重要技术设施、重要行业商业机密等
攻击手段	攻击手段单一，常基于已有的恶意软件展开攻击	攻击手段复杂隐蔽、形式多样，结合特制工具、社会工程学等展开攻击
攻击时间	攻击时间较短，以一次性、大范围攻击为主	攻击时间较长、长期潜伏、多次渗透、低频回传
攻击影响	通常造成短期的系统中断或数据泄露，对个人用户和企业的运营产生一定的干扰和损失	可对关键设施造成长期的破坏，在国际政治和军事冲突中发挥重要作用，甚至影响全球的安全形势
追溯难度	攻击特性强，容易在短时间内被检测和捕获	攻击特征弱，隐蔽性强，缺少样本数据，很难被检测和捕获

数据集	年份	主机操作系统	数据格式	大小
LANL^[5]	2015	Windows	.txt	160 GB
Streamspot^[6]	2016	Linux	.tsv	2.8 GB
Unicorn SC^[7]	2020	Linux	.txt	147 GB
DARPA TC E3^[8]	2018	Windows, Linux, FreeBSD, Android	CDM v18	116 GB
DARPA TC E5^[8]	2019	Windows, Linux, FreeBSD, Android	CDM v20	340 GB
DARPA OpTC^[9]	2020	Windows 10	eCar	1TB
ATLAS^[10]	2021	Windows	.txt	6.5 GB
ATLASv2^[11]	2023	Windows	.txt	154 GB
Nodlink SD^[12]	2024	Windows, Linux	.json	15 GB
Kellect4APT^[13]	2024	Windows	.csv	18.5 GB
Linux-APT^[40]	2024	Linux	.csv	205 MB

数据集	真实世界模拟程度
数据集	良性数据来源	攻击面	主机数 /个	总事件 /件	恶意事件 /件	时间跨度
LANL^[5]	真实	DNS	17684	10亿	0.3亿	56天
Streamspot^[6]	模拟	Firefox	1	0.6亿	0.45亿	1天
Unicorn SC^[7]	模拟	CI服务器	1	2.8亿	480万	3天
DARPA TC E3^[8]	模拟	Firefox SMB Nginx	7	20亿	0.5亿	10天
DARPA TC E5^[8]	模拟	Firefox SMB Nginx	18	120亿	2.5亿	9天
DARPA OpTC^[9]	模拟	Powershell	500	170亿	3亿	7天
ATLAS^[10]	模拟	Flash Word	2	249万	17万	1天
ATLASv2^[11]	模拟	Flash Word	2	27637	18146	5天
Nodlink SD^[12]	模拟	Apache Struts2 MSSQL	300	4116	190	5天
Kellect4APT^[13]	模拟	Firefox SMB Nginx Flash Word	1	5100万	49万	1天
Linux-APT^[40]	模拟	Flash Word	2	10万	2.5万	6周
数据集	APT攻击生命周期覆盖度	标注粒度与质量	文档完整性	—	—	—
LANL^[5]	完整	实体	×	—	—	—
Streamspot^[6]	部分	图	×	—	—	—
Unicorn SC^[7]	部分	图	×	—	—	—
DARPA TC E3^[8]	部分	实体	×	—	—	—
DARPA TC E5^[8]	部分	实体	×	—	—	—
DARPA OpTC^[9]	部分	实体	×	—	—	—
ATLAS^[10]	完整	实体	×	—	—	—
ATLASv2^[11]	完整	实体	×	—	—	—
Nodlink SD^[12]	完整	实体	×	—	—	—
Kellect4APT^[13]	完整	TTP	×	—	—	—
Linux-APT^[40]	完整	实体	×	—	—	—

数据集	覆盖技术数	复杂度	标注字段	标注方法
LANL^[5]	中等	高	登录事件、进程ID、网络流	红队演练日志映射
Streamspot^[6]	简单	低	仅标记恶意/良性图结构	脚本执行自动生成
Unicorn SC^[7]	中等	中	CamFlow审计日志	基于攻击阶段人工批次划分
DARPA TC E3^[8]	中等	中	PID、文件路径、Socket、系统调用	红队日志和专家手工映射
DARPA TC E5^[8]	中等	中	完整CDM字段（进程、文件、网络等）	专家根据攻击方案手工标记
DARPA OpTC^[9]	完整	高	eCar格式（进程、文件、注册表、网络等）	基于红队操作日志的自动映射
ATLAS^[10]	完整	中	核心受损实体（进程命令行、文件路径）	专家人工选择性标注
ATLASv2^[11]	完整	高	多源日志（Sysmon 树、Carbon Black 审计）	真实操作过程嵌入人工标记
Nodlink SD^[12]	中等	中	事件语义（Web Socket连接、文件写入）	基于脚本触发时间人工校验
Kellect4APT^[13]	完整	中	TTP ID、应用调用栈、内核级事件	基于原子红队工具半自动标注
Linux-APT^[40]	完整	低	告警信息(源IP、文件、规则描述)	基于安全预警规则后验标记

检测方法	系统	检测新型攻击	假阳率	及时性	攻击还原	可解释性	告警粒度
图匹配方法	Provg-Searcher^[47]	○	◑	○	×	○	图
图匹配方法	MEGR-APT^[48]	○	◑	○	×	○	图
基于规则匹配的方法	APTSHIELD^[49]	○	◑	●	√	●	节点
	CAPTAIN^[50]	○	◑	◑	×	◑	节点
	CAPTAIN+^[51]	○	◑	●	×	◑	节点
基于重建的图学习方法	ProGrapher^[52]	●	◑	●	×	○	节点
	ThreaTrace^[39]	●	◑	●	×	○	节点
	FLASH^[53]	●	◑	●	√	◑	节点
	MAGIC^[16]	●	○	◑	×	○	节点
	APT-SSC^[54]	●	○	●	×	○	节点
	LT-ProveGD^[55]	●	○	●	×	○	节点
	KAIROS^[17]	●	◑	●	√	◑	图
	JBEIL^[56]	●	◑	●	×	○	节点
	Nodlink^[12]	●	○	●	√	◑	节点
	R-CAID^[57]	●	◑	●	×	○	节点
	ShadeWatcher^[18]	●	◑	◑	×	◑	边
	VELOX^[58]	●	○	●	×	◑	边
	ORTHRUS^[38]	●	○	●	√	◑	边
基于分类的图学习方法	Prov-gem^[59]	●	◑	●	×	○	节点
	ATLAS^[10]	◑	○	◑	×	◑	路径
	TREC^[13]	●	○	●	√	●	攻击手段
	APT-KGL^[60]	◑	◑	◑	×	○	节点
	OCR-APT^[21]	●	○	●	√	◑	节点
	STGAN^[61]	◑	○	●	√	○	节点
	Slot^[62]	◑	○	●	√	◑	节点
基于LLM的方法	APT-LLM^[22]	●	○	◑	×	○	节点
	OMNISEC^[20]	◑	○	◑	×	◑	节点
	SHIELD^[23]	◑	○	◑	√	●	节点
	SAGE^[25]	●	○	●	√	●	节点