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，我们的沟通和工作方式发生了显著变化。虽然这带来了新的机遇，但也增加了网络威胁。一种常见且严重的威胁是网络钓鱼，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内容的先进检测模型，解决了这一紧迫问题。我们提出的方法整合了专门用于结构化表格数据的多层感知器（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。来自这些模型的嵌入通过一种新颖的融合过程和谐地组合在一起，随后将融合后的嵌入输入到线性分类器中。考虑到近期用于全面钓鱼研究的数据集稀缺，我们的贡献还包括创建了一个最新数据集，并公开与社区分享。该数据集经过精心策划以反映真实的钓鱼场景，确保其相关性和适用性。研究结果凸显了所提方法的有效性，其中CANINE在分析页面标题方面表现出卓越性能，而RoBERTa在评估页面内容方面尤为出色。融合两个NLP模型与一个MLP模型的MultiText-LP方法在实验数据集上取得了令人瞩目的成果，F1分数达96.80，准确率高达97.18%。此外，我们的方法在CatchPhish HTML数据集上优于现有方法，展示了其高效性。