基于层次化掩码图自编码框架的APT威胁检测方法

刘尚东; 杨易润; 王一诺; 杜宏煜; 汪文博; 季一木

doi:10.12263/DZXB.20251261

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基于层次化掩码图自编码框架的APT威胁检测方法

更新时间：2026-06-15

- 基于层次化掩码图自编码框架的APT威胁检测方法
- Advanced Persistent Threat Detection Via Hierarchical Masking Graph Autoencoder
- 电子学报 2026年页码：1-17
- 作者机构：
  
  1.南京邮电大学计算机学院，江苏南京 210023
  2.南京邮电大学高性能计算与大数据处理研究所，江苏南京 210023
  3.国家高性能计算中心南京分中心，江苏南京 210023
  4.江苏省高性能计算与智能处理工程研究中心，江苏南京 210023
- 作者简介：
  
  刘尚东男，1979年10月出生于甘肃省永靖县。现为南京邮电大学副教授、硕士生导师，主要方向为网络空间安全、人工智能、大数据等。 E-mail: lsd@njupt.edu.cn
  杨易润男，2001年12月出生于江苏省无锡市。现为南京邮电大学计算机学院、软件学院、网络空间安全学院硕士研究生，主要研究方向为APT检测与溯源。 E-mail: 1024041132@njuput.edu.cn
  王一诺女，2003年11月出生于江苏省连云港市。现为南京邮电大学计算机学院、软件学院、网络空间安全学院硕士研究生，主要研究方向为网络安全态势感知。 E-mail: 1025040932@njupt.edu.cn
  杜宏煜男，2000年11月出生于江苏省盐城市。现为南京邮电大学计算机学院、软件学院、网络空间安全学院博士研究生，主要研究方向为漏洞检测和网络空间威胁感知。E-mail: 2024040408@njupt.edu.cn
  汪文博男，1998年6月出生于江苏省苏州市。现为南京邮电大学计算机学院、软件学院、网络空间安全学院在读博士，主要研究方向为计算机网络安全和机器学习等。 E-mail: wangwenbo@njupt.edu.cn
  季一木男，1978年9月出生于安徽省无为市。现为南京邮电大学计算机学院教授，博士生导师，主要研究方向为人工智能、云计算和大数据安全等。 E-mail: iym@njupt.edu.cn
- 基金信息：
  
  国家重点研发计划课题(2023YFB2904000;2023YFB2904004);江苏省重点研发计划课题(BE2023004-2)
- DOI：10.12263/DZXB.20251261
  中图分类号： TP393;
- 收稿：2026-03-29，
  
  录用：2026-05-05，
  
  网络首发：2026-06-15，
- 稿件说明：
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刘尚东, 杨易润, 王一诺, 等. 基于层次化掩码图自编码框架的APT威胁检测方法[J/OL]. 电子学报, 2026,1-17.

LIU Shangdong, YANG Yirun, WANG Yinuo, et al. Advanced Persistent Threat Detection Via Hierarchical Masking Graph Autoencoder[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-17.
刘尚东, 杨易润, 王一诺, 等. 基于层次化掩码图自编码框架的APT威胁检测方法[J/OL]. 电子学报, 2026,1-17. DOI： 10.12263/DZXB.20251261.

LIU Shangdong, YANG Yirun, WANG Yinuo, et al. Advanced Persistent Threat Detection Via Hierarchical Masking Graph Autoencoder[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-17. DOI： 10.12263/DZXB.20251261.

摘要

高级持续性威胁（Advanced Persistent Threats， APTs）凭借其高度隐蔽性、长周期性及多阶段攻击的特性，已成为当前网络安全防御体系面临的最严峻挑战之一。尽管基于主机日志的溯源图分析技术能够将孤立的系统事件关联为细粒度的行为审计路径，为威胁检测提供了结构化支撑，但现有研究仍面临核心瓶颈：在复杂的系统环境中，攻击者往往通过低频操作来模拟良性行为，导致传统的基于特征码或静态规则的检测方案在应对零日攻击（Zero-day）时极易失效。针对上述挑战，本文提出一种层次化感知的图掩码自动编码器APT威胁检测框架。本框架的核心创新在于引入了层次化拓扑知识来指导掩码过程，而非采用盲目的随机遮蔽。具体而言，模型集成了全局感知遮蔽、局部感知遮蔽与元素感知遮蔽三种策略：全局感知遮蔽旨在保留溯源图的宏观结构稳定性，局部感知遮蔽侧重于刻画实体间的邻域交互逻辑，而元素感知遮蔽则关注实体属性的细粒度特征。这种层次化设计能够在预训练阶段有效过滤非结构性的系统噪声，同时最大程度地保留关键的因果逻辑链条。特别地，节点级一致性约束通过在原子尺度上建模，有效规避了传统图表征学习中全局聚合带来的信号稀释风险。这确保了即便在极端不平衡的样本分布下，微弱的攻击信号仍能通过损失函数获得充分的梯度响应，从而在数学逻辑上保障了训练目标与单点异常检测任务的一致性。在检测阶段，框架采用无监督异常检测算法，基于实体类型的嵌入分布量化节点异常分数，从而精准识别破坏局部因果链的恶意行为。本文在StreamSpot、Unicorn Wget以及DARPA E3等多个公开权威数据集上进行了全面评估。实验结果表明，该框架在平均精确率上达到了98.49%，F1分数达到98.97%。相比于现有基准模型，本文方法在极低攻击基本比率场景下表现出更强的鲁棒性与召回能力，能够有效识别APT攻击全生命周期中的微弱异常信号。

Abstract

Advanced Persistent Threats (APTs) have emerged as one of the most severe challenges to modern cybersecurity defense systems due to their extreme stealthiness

prolonged duration

and multi-stage nature. Although provenance-based analysis of host logs provides structured support for threat detection by correlating isolated system events into granular behavioral auditing paths

existing research still faces a core bottleneck: in complex system environments

attackers often disguise malicious activities as benign behavior through low-frequency operations

rendering traditional detection schemes based on signatures or static rules highly susceptible to failure when encountering zero-day attacks. To address these challenges

this paper proposes a Hierarchical-Aware Graph Masked Autoencoder framework for APT detection. The primary innovation of this framework lies in the introduction of hierarchical topological knowledge to guide the masking process

fundamentally overcoming the limitations of blind random masking. Specifically

the model integrates three targeted strategies: Global-Aware Masking (GAM)

Local-Aware Masking (LAM)

and Element-Aware Masking (EAM). GAM aims to preserve the macro-structural stability of the provenance graph; LAM focuses on characterizing neighborhood interaction logic between entities; and EAM addresses fine-grained entity attributes. This hierarchical design effectively filters out non-structural system noise during the pre-training phase while maximizing the retention of critical causal logic chains. Notably

the node-level consistency constraint models at an atomic scale

effectively circumventing the risk of signal dilution caused by global aggregation in traditional graph representation learning. This ensures that even under extremely imbalanced sample distributions

faint attack signals can still obtain sufficient gradient responses through the loss function

thereby mathematically guaranteeing the logical alignment between training objectives and point-wise anomaly detection tasks. During the detection phase

the framework employs an unsupervised anomaly detection algorithm to quantify node anomaly scores based on the embedding distributions of entity types

enabling the precise identification of malicious behaviors that disrupt local causal chains. Comprehensive evaluations were conducted on multiple authoritative public datasets

including StreamSpot

Unicorn Wget

and DARPA E3. Experimental results demonstrate that the proposed framework achieves an average precision of 98.49% and an F1-score of 98.97%. Compared to state-of-the-art baselines

our method exhibits superior robustness and recall in scenarios with extremely low attack base rates

effectively identifying subtle anomalous signals throughout the entire APT lifecycle.

关键词

Keywords

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