网络钓鱼检测技术的研究与改进
摘 要
本研究旨在提出一种改进的网络钓鱼检测方法，以提升检测精度和效率具体而言，本文首先分析了现有网络钓鱼检测技术的局限性，包括特征提取能力不足、模型泛化性能较差以及实时性难以满足需求等问题基于此，研究提出了一种融合多源特征与深度学习的检测框架该框架通过结合静态特征（如URL结构、HTML内容）和动态行为特征（如用户交互模式），构建了更为全面的特征表示同时，引入注意力机制优化深度神经网络模型，以增强对关键特征的学习能力实验结果表明，所提方法在多个公开数据集上均取得了显著优于传统方法的检测性能，F1分数提升了约8%，并且具备较强的鲁棒性和适应性此外，为解决实际应用中的冷启动问题，本文设计了一种轻量级在线更新机制，能够在保证检测效果的同时降低计算开销。
关键词：网络钓鱼检测 多源特征融合 深度学习
Abstract
This study aims to propose an improved phishing detection method, To improve the detection accuracy and efficiency, specifically, This paper first analyzes the limitations of existing phishing detection techniques, Based on this problem, including the lack of feature extraction ability, poor model generalization performance, and difficult to meet the requirements of real-time performance, The detection fr amework, by combining static features (e. g., URL structure, HTML content) and dynamic behavioral features (e. g., user interaction mode), A more comprehensive feature representation was constructed and simultaneously, Introducing attention mechanisms to optimize deep neural network models, Experimental results on enhanced learning of key features show that, The proposed method achieves significantly better detection performance than conventional methods on several publicly available datasets, The F1 score was improved by about 8%, And has a strong robustness and adaptability in addition, To solve the cold-start problem in practical applications, In this paper, we design a lightweight online update mechanism, It can reduce the computational overhead while ensuring the detection effect.
Keyword:Phishing Detection Multi-Source Feature Fusion Deep Learning
目 录
引言 1
1网络钓鱼检测技术概述 1
1.1网络钓鱼的基本概念 1
1.2检测技术的发展历程 2
1.3当前主要检测方法分类 2
1.4技术面临的挑战与局限 3
2数据驱动的检测模型研究 3
2.1数据特征提取与选择 3
2.2机器学习在检测中的应用 3
2.3深度学习模型的改进方向 4
2.4模型性能评估指标分析 4
2.5数据不平衡问题的应对策略 5
3实时检测技术的优化方案 5
3.1实时检测的关键需求 5
3.2基于规则的快速匹配算法 5
3.3动态行为分析技术研究 6
3.4跨平台实时检测框架设计 6
3.5性能与安全性的平衡探讨 6
4用户行为与社会工程防御 7
4.1用户行为模式的建模方法 7
4.2社会工程攻击的特点分析 7
4.3行为异常检测算法设计 8
4.4用户教育与意识提升策略 8
4.5综合防御体系的构建思路 8
结论 9
参考文献 10
致谢 11
摘 要
本研究旨在提出一种改进的网络钓鱼检测方法，以提升检测精度和效率具体而言，本文首先分析了现有网络钓鱼检测技术的局限性，包括特征提取能力不足、模型泛化性能较差以及实时性难以满足需求等问题基于此，研究提出了一种融合多源特征与深度学习的检测框架该框架通过结合静态特征（如URL结构、HTML内容）和动态行为特征（如用户交互模式），构建了更为全面的特征表示同时，引入注意力机制优化深度神经网络模型，以增强对关键特征的学习能力实验结果表明，所提方法在多个公开数据集上均取得了显著优于传统方法的检测性能，F1分数提升了约8%，并且具备较强的鲁棒性和适应性此外，为解决实际应用中的冷启动问题，本文设计了一种轻量级在线更新机制，能够在保证检测效果的同时降低计算开销。
关键词：网络钓鱼检测 多源特征融合 深度学习
Abstract
This study aims to propose an improved phishing detection method, To improve the detection accuracy and efficiency, specifically, This paper first analyzes the limitations of existing phishing detection techniques, Based on this problem, including the lack of feature extraction ability, poor model generalization performance, and difficult to meet the requirements of real-time performance, The detection fr amework, by combining static features (e. g., URL structure, HTML content) and dynamic behavioral features (e. g., user interaction mode), A more comprehensive feature representation was constructed and simultaneously, Introducing attention mechanisms to optimize deep neural network models, Experimental results on enhanced learning of key features show that, The proposed method achieves significantly better detection performance than conventional methods on several publicly available datasets, The F1 score was improved by about 8%, And has a strong robustness and adaptability in addition, To solve the cold-start problem in practical applications, In this paper, we design a lightweight online update mechanism, It can reduce the computational overhead while ensuring the detection effect.
Keyword:Phishing Detection Multi-Source Feature Fusion Deep Learning
目 录
引言 1
1网络钓鱼检测技术概述 1
1.1网络钓鱼的基本概念 1
1.2检测技术的发展历程 2
1.3当前主要检测方法分类 2
1.4技术面临的挑战与局限 3
2数据驱动的检测模型研究 3
2.1数据特征提取与选择 3
2.2机器学习在检测中的应用 3
2.3深度学习模型的改进方向 4
2.4模型性能评估指标分析 4
2.5数据不平衡问题的应对策略 5
3实时检测技术的优化方案 5
3.1实时检测的关键需求 5
3.2基于规则的快速匹配算法 5
3.3动态行为分析技术研究 6
3.4跨平台实时检测框架设计 6
3.5性能与安全性的平衡探讨 6
4用户行为与社会工程防御 7
4.1用户行为模式的建模方法 7
4.2社会工程攻击的特点分析 7
4.3行为异常检测算法设计 8
4.4用户教育与意识提升策略 8
4.5综合防御体系的构建思路 8
结论 9
参考文献 10
致谢 11
