基于双路特征提取和自适应融合的钓鱼网站检测

郭凯; 王恒; 马自强

doi:10.11959/j.issn.2096-109x.2025076

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基于双路特征提取和自适应融合的钓鱼网站检测

学术论文 | 更新时间：2026-05-26

- 基于双路特征提取和自适应融合的钓鱼网站检测
- Phishing website detection based on two-way feature extraction and adaptive fusion
- 网络与信息安全学报 2026年12卷第1期页码：88-103
- 作者机构：
  
  宁夏大学信息工程学院，宁夏银川 750021
- 作者简介：
  
  郭凯（1999- ），男，山西吕梁人，宁夏大学硕士生，主要研究方向为网络空间安全。
  王恒（1975- ），男，宁夏银川人，博士，宁夏大学教授，主要研究方向为网络空间安全、自然语言处理。
  马自强（1990- ），男，新疆乌鲁木齐人，博士，宁夏大学副教授，主要研究方向为密码应用、系统安全、区块链、恶意流量识别、网络舆情分析。
- 基金信息：
  
  国家自然科学基金(62463025);宁夏回族自治区重点研发计划一般项目(2022BDE03008)
- DOI：10.11959/j.issn.2096-109x.2025076
  中图分类号： TP393.08
- 收稿：2025-03-05，
  
  修回：2025-12-21，
  
  纸质出版：2026-02-25
- 稿件说明：
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郭凯, 王恒, 马自强. 基于双路特征提取和自适应融合的钓鱼网站检测[J]. 网络与信息安全学报, 2026, 12(1): 88-103.

Guo Kai, Wang Heng, Ma Ziqiang. Phishing website detection based on two-way feature extraction and adaptive fusion[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 88-103.
郭凯, 王恒, 马自强. 基于双路特征提取和自适应融合的钓鱼网站检测[J]. 网络与信息安全学报, 2026, 12(1): 88-103. DOI： 10.11959/j.issn.2096-109x.2025076.

Guo Kai, Wang Heng, Ma Ziqiang. Phishing website detection based on two-way feature extraction and adaptive fusion[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 88-103. DOI： 10.11959/j.issn.2096-109x.2025076.

摘要

随着网络钓鱼攻击手段的不断演进，传统检测方法在特征提取、模型泛化能力等方面面临严峻挑战。针对这些问题，提出了一种基于双路特征提取与自适应融合的钓鱼网站检测模型（DPFEAT）。首先，构建了时序与空间双路特征提取机制，通过引入特征交互层增强整体特征的表征能力；其次，设计了自适应融合模块，用于实现多维特征的动态融合，从而有效提升模型对复杂攻击特征的捕获能力。在训练策略上，采用预热－余弦衰减和循环学习率调度机制，显著增强了训练过程的稳定性。同时，结合Focal Loss与Dice Loss构建组合损失函数，并引入动态权重平衡机制，有效缓解了类别不平衡与边界样本识别困难的问题。此外，还设计了可解释性分析框架，用于评估各模块对模型性能的贡献。实验结果表明，DPFEAT在准确率与受试者工作特征曲线下面积（AUC-ROC）值上分别达到95.41%和98.83%，显著优于现有方法。

Abstract

With the continuous evolution of phishing attack methods

traditional detection approaches face severe challenges in terms of feature extraction and model generalization capability. To address these issues

a phishing detection model named DPFEAT (phishing detection model with dual-path feature extraction and adaptive fusion)

which is based on dual-path feature extraction and adaptive fusion

was proposed. Firstly

a dual-path feature extraction mechanism for temporal and spatial features was constructed

and a feature interaction layer was introduced to enhance the overall feature representation capability. Secondly

an adaptive fusion module was designed to achieve dynamic fusion of multi-dimensional features

thereby effectively improving the model's ability to capture complex attack characteristics. Regarding the training strategy

a warmup-cosine decay and cyclical learning rate scheduling mechanism was adopted

which significantly improved the stability of the training process. Simultaneously

a combined loss function was constructed by integrating Focal Loss and Dice Loss

and a dynamic weight balancing mechanism was introduced

which effectively alleviated the issues of class imbalance and the difficulty in identifying boundary samples. Furthermore

an interpretability analysis framework was designed to evaluate the contribution of each module to the model's performance. Experimental results demonstrated that the proposed DPFEAT model achieved an accuracy of 95.41% and an receiver operating characteristicarea under the curve (AUC-ROC) score of 98.83%

which is significantly superior to existing methods.

关键词

Keywords

references

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