文献类型：期刊文献

英文题名：Advancing semi-supervised phishing website detection in noisy conditions via optimal transport

作者：Wang, Zipeng[1];Li, Kunpeng[1];Song, Andy[1];Xun, Zichao[1];Zhou, Qin[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Meilong Rd, Shanghai 200030, Peoples R China

年份：2026

卷号：56

期号：5

外文期刊名：APPLIED INTELLIGENCE

收录：;EI(收录号：20261420421516);WOS:【SCI-EXPANDED(收录号:WOS:001721104800001)】；

基金：This work was supported by the National College Students Innovation and Entrepreneurship Training Program grant 202410251054.

语种：英文

外文关键词：Phishing URL detection; Cybersecurity defense; Semi-supervised learning; Optimal transport theory; Learning from noisy labels

摘要：Phishing attacks pose significant threats to personal and financial security, as cybercriminals employ deceptive websites to steal sensitive information. Existing methods heavily depend on manually labeled datasets, which are expensive to construct, prone to labeling noise, and quickly become outdated due to the rapid emergence of new URLs. This limitation hinders model adaptability to evolving phishing strategies. To address these challenges, we propose a novel semi-supervised phishing detection framework that exploits contextual relationships among URLs through optimal transport theory, Sinkhorn optimization, and task-specific loss functions. By enhancing pseudo-labeling quality for unlabeled data, our method effectively mitigates the impact of noisy annotations and improves model robustness. Extensive experiments conducted on benchmark phishing datasets demonstrate the superior performance of our approach, achieving up to a 5% improvement in F1-score under 40% label noise compared with state-of-the-art baselines.