探索密流分析鲜度模型：从“数据留存”到“价值沉淀”

卞华峰; 郑纪成; 马小博; 刘宏桃; 管晓宏

doi:10.12263/DZXB.20251008

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探索密流分析鲜度模型：从“数据留存”到“价值沉淀”

密态网络安全前沿技术 | 更新时间：2026-07-30

- 探索密流分析鲜度模型：从“数据留存”到“价值沉淀”
- Exploring Freshness Modeling for Encrypted Traffic Analysis: From “Data Retention” to “Value Accumulation”
- 电子学报 2026年54卷第4期页码：1446-1459
- 作者机构：
  
  1.西安交通大学电子与信息学部，陕西西安 710049
  2.智能网络与网络安全教育部重点实验室，陕西西安 710049
  3.陕西省计算机网络重点实验室，陕西西安 710049
- 作者简介：
  
  卞华峰男，1997年8月出生于江苏省淮安市。现为西安交通大学计算机科学与技术学院博士研究生。主要研究方向为流量分析、无线安全。 E-mail: t1071680825@stu.xjtu.edu.cn
  郑纪成男，2001年8月出生于河北省承德市。现为西安交通大学计算机科学与技术学院博士研究生。主要研究方向为流量分析。 E-mail: 3123151037@stu.xjtu.edu.cn
  马小博男，1983年9月出生于陕西省宝鸡市。现为西安交通大学电信学部教授、博士生导师。主要研究方向为网络安全与隐私、网络测量。中国电子学会会员编号：E190036656M。 E-mail: xma.cs@xjtu.edu.cn
  刘宏桃男，1997年6月出生于山西省吕梁市。2024年毕业于西安交通大学软件学院，获工学硕士学位。主要研究方向为网络安全。 E-mail: lht974868031@stu.xjtu.edu.cn
  管晓宏男，1955年11月出生于四川省泸州市。现为西安交通大学电信学部教授、博士生导师，中国科学院院士。主要研究方向为复杂网络化系统的经济性与安全性，电力、能源、制造系统的优化，网络空间安全，信息物理融合系统等。中国电子学会会员编号：E190184944M。E-mail: xhguan@xjtu.edu.cn
- 基金信息：
  
  国家自然科学基金(U23A20332;62272381);陕西省杰出青年科学基金(2023-JC-JQ-50)
- DOI：10.12263/DZXB.20251008
  中图分类号： TP393.08;
- 收稿：2026-01-08，
  
  录用：2026-02-09，
  
  网络首发：2026-05-20，
  
  纸质出版：2026-04-25
- 稿件说明：
移动端阅览
卞华峰, 郑纪成, 马小博, 等. 探索密流分析鲜度模型：从“数据留存”到“价值沉淀”[J]. 电子学报, 2026, 54(04): 1446-1459.

BIAN Huafeng, ZHENG Jicheng, MA Xiaobo, et al. Exploring Freshness Modeling for Encrypted Traffic Analysis: From “Data Retention” to “Value Accumulation”[J]. Acta Electronica Sinica, 2026, 54(04): 1446-1459.
卞华峰, 郑纪成, 马小博, 等. 探索密流分析鲜度模型：从“数据留存”到“价值沉淀”[J]. 电子学报, 2026, 54(04): 1446-1459. DOI：10.12263/DZXB.20251008

BIAN Huafeng, ZHENG Jicheng, MA Xiaobo, et al. Exploring Freshness Modeling for Encrypted Traffic Analysis: From “Data Retention” to “Value Accumulation”[J]. Acta Electronica Sinica, 2026, 54(04): 1446-1459. DOI：10.12263/DZXB.20251008

摘要

众所周知，网络流量呈海量化与密态化发展趋势。受限于计算与存储资源，流量分析系统难以对高速海量密流进行在线建模并全量存储，通常采用“先存后筛”的序贯策略，即先完整留存目标密流后，再筛选代表性样本开展分析。此策略依赖人工定义特定留存目标（如特定IP地址范围），难以应对流量的动态演化特性，导致留存数据的边际价值不断递减。即使人为提升留存目标多样性，由于缺乏系统化价值评价体系，不同留存数据仍难以实现价值沉淀，无法反向优化数据留存策略。为此，本文提出一种密流分析鲜度方法（FreshHunter）。该方法从时间、空间与语义三个维度系统化建模密流鲜度，时间维度采用牛顿水冷定律刻画密流的自然老化特征，空间和语义维度提取跨类型、跨行为的通用个体结构特征。进一步地，FreshHunter构建了鲜度感知的对比学习框架，通过引入可学习类中心，增强密流特征嵌入与类中心间的一致性，确保密流样本群体多样性，并实现“边筛边存”的高鲜度密流样本在线留存机制。在留存过程中，FreshHunter将高鲜度密流的特征嵌入不断聚合至可学习类中心，以构建代表性密流特征的统一表示；同时结合特征漂移检测与自适应校正机制，动态更新类中心，以捕捉密流特征的时序性演化。由此，FreshHunter同时捕捉高价值、代表性的密流样本及其演化，促成了从瞬时数据捕获到长期知识累积的升级，为下游密流分析提供前向知识沉淀、后向持续优化的数据支撑。本文在真实数据集上开展广泛评估，覆盖物联网（Internet of Things，IoT）终端、手机App和网站识别三类典型密流分析任务及九种主流分析算法。实验结果表明，FreshHunter可提升下游密流分析算法的

分数约15%~60%，优于Random、动态时间规整（Dynamic Time Warping，DTW）、树模型沙普利加性解释（Tree SHAPley additive explanations，TreeSHAP）三种基线密流筛选方法。在特征漂移场景下，FreshHunter仍保持较强适应性，提升

分数约20%。本研究为资源受限条件下密流分析的数据利用率和模型性能增强提供了新的技术路径。

Abstract

As is well known

network traffic has become increasingly massive and encrypted. Due to limitations in computational and storage resources

traffic analysis systems struggle to perform online modeling and full-volume storage of high-speed encrypted traffic. Existing systems typically adopt a “store-then-select” strategy

wherein all target encrypted traffic is fully retained before being selected for analysis. However

this strategy relies on manually defined retention targets (e.g.

specific IP ranges)

making it difficult to adapt to dynamic traffic evolution and resulting in a diminishing marginal value of retained data. Despite deliberate efforts to increase the diversity of retention targets

the absence of a systematic value evaluation framework hinders the accumulation of

data value and precludes the feedback-driven optimization of retention strategies. To address these challenges

this paper proposes FreshHunter

a freshness modeling approach for encrypted traffic analysis. FreshHunter systematically models the freshness of encrypted traffic from three dimensions: temporal

spatial

and semantic. The temporal dimension adopts Newton’s law of cooling to describe the natural aging of encrypted traffic

whereas the spatial and semantic dimensions extract structural features that are universal across types and behaviors. In addition

FreshHunter constructs a freshness-aware contrastive learning framework that incorporates learnable class centers to enhance the consistency between traffic embeddings and their corresponding centers

preserve the diversity of traffic samples

and thereby enable a “select-while-storing” mechanism for retaining high-freshness samples. During retention

FreshHunter continuously aggregates the embeddings of high-freshness encrypted traffic into learnable class centers

thereby forming a unified representation of representative traffic features. Meanwhile

it employs feature drift detection and adaptive correction mechanisms to dynamically update these class centers and capture the temporal evolution of traffic features. Thus

FreshHunter captures high-value

representative encrypted traffic samples along with their temporal evolution

facilitating an upgrade from instantaneous data capture to sustained knowledge accumulation

while supporting forward knowledge consolidation and backward adaptive optimization for downstream encrypted traffic analysis. Comprehensive evaluations are conducted on real-world datasets covering three representative encrypted traffic analysis tasks (i.e.

internet of things (IoT) device

mobile App

and website identification) across nine mainstream analysis algorithms. Experimental results show that FreshHunter improves the

-score of downstream encrypted traffic analysis algorithms by approximately 15

% to 60%

outperforming the three baseline traffic selection methods: Random

dynamic time warping (DTW)

and tree shapley additive explanations (TreeSHAP). Even under traffic feature drift scenarios

FreshHunter maintains strong adaptability

improving algorithm performance by around 20%. Overall

this study offers a novel technical pathway for advancing data efficiency and model adaptability in encrypted traffic analysis under resource-constrained conditions.

关键词

Keywords

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