.png){kind=icon}
返回
Enhanced phishing detection using multimodal data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2025.115105.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Phishing remains one of the most persistent cybersecurity threats, increasingly exploiting not only technical vulnerabilities but also human cognitive biases. Existing detection systems often rely on single-modality features and black-box models, which restrict both generalization and interpretability. This study presents an explainable multimodal framework that combines textual and technical cues, including message content, URL structure, and Principles of Persuasion, to capture both objective and subjective aspects of phishing. Several classifiers were evaluated using 10-fold stratified cross-validation, with Random Forest achieving the best balance between performance and transparency (ROC-AUC = 0.9840), supported by SHAP explanations that identify the most influential linguistic and structural features. Comparative analysis shows that the proposed framework outperforms unimodal baselines while preserving interpretability, enabling a clear rationale for classification outcomes. These results indicate that integrating multimodal representation with explainable learning strengthens phishing detection accuracy, improves user trust, and supports reliable deployment in real-world environments.
Keyword:
Phishing detection
Multimodal learning
Explainable artificial intelligence
Principles of Persuasion
Human-centered cybersecurity
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Classification of Phishing Email Using Random Forest Machine Learning Technique基于随机森林机器学习技术的钓鱼邮件分类
OAAI
OAAI分享收藏Multimodal fraudulent website identification method based on heterogeneous model ensemble.png)
CHINA COMMUNICATIONS
CHINA COMMUNICATIONSIF3.1
PREAI
分享收藏
查看更多内容