The way we communicate and work has changed significantly with the rise of the Internet. While it has opened up new opportunities, it has also brought about an increase in cyber threats. One common and serious threat is phishing, where cybercriminals employ deceptive methods to steal sensitive information.This study addresses the pressing issue of phishing by introducing an advanced detection model that meticulously focuses on HTML content. Our proposed approach integrates a specialized Multi-Layer Perceptron (MLP) model for structured tabular data and two pretrained Natural Language Processing (NLP) models for analyzing textual features such as page titles and content. The embeddings from these models are harmoniously combined through a novel fusion process. The resulting fused embeddings are then input into a linear classifier. Recognizing the scarcity of recent datasets for comprehensive phishing research, our contribution extends to the creation of an up-to-date dataset, which we openly share with the community. The dataset is meticulously curated to reflect real-life phishing conditions, ensuring relevance and applicability. The research findings highlight the effectiveness of the proposed approach, with the CANINE demonstrating superior performance in analyzing page titles and the RoBERTa excelling in evaluating page content. The fusion of two NLP and one MLP model,termed MultiText-LP, achieves impressive results, yielding a 96.80 F1 score and a 97.18 accuracy score on our research dataset. Furthermore, our approach outperforms existing methods on the CatchPhish HTML dataset, showcasing its efficacies.

翻译：随着互联网的兴起，我们的交流与工作方式已发生显著变化。它在开辟新机遇的同时，也带来了网络威胁的增多。网络钓鱼是一种常见且严重的威胁，网络犯罪分子通过欺骗手段窃取敏感信息。本研究通过引入一种专注于HTML内容的高级检测模型，应对这一紧迫问题。我们提出的方法集成了一个用于处理结构化表格数据的专用多层感知器（MLP）模型，以及两个预训练的自然语言处理（NLP）模型，用于分析页面标题和内容等文本特征。这些模型生成的嵌入表示通过一种新颖的融合过程进行有效整合。融合后的嵌入表示随后输入线性分类器。鉴于当前缺乏用于全面钓鱼研究的近期数据集，我们的贡献还包括创建了一个最新的数据集，并公开分享给研究社区。该数据集经过精心整理，以反映真实的钓鱼场景，确保其相关性和适用性。研究结果突显了所提方法的有效性：CANINE模型在分析页面标题方面表现出优异性能，而RoBERTa模型在评估页面内容方面表现卓越。融合两个NLP模型与一个MLP模型（称为MultiText-LP）取得了令人印象深刻的结果，在我们的研究数据集上实现了96.80的F1分数和97.18的准确率。此外，我们的方法在CatchPhish HTML数据集上优于现有方法，充分展示了其效能。