一种基于信息论的创新方法，用于解决并增强钓鱼检测的透明度
An Innovative Information Theory-based Approach to Tackle and Enhance The Transparency in Phishing Detection
AI总结：
针对AI钓鱼检测缺乏可解释性的问题，提出基于深度学习的邮件钓鱼攻击定位方法，可同时预测风险并定位关键钓鱼相关信息，在7个真实数据集上较基线性能提升1.5%-3.5%。
摘要：
钓鱼攻击已成为检测、解释与防御层面的严峻难题。尽管针对钓鱼攻击的研究已开展十余年，涵盖技术与非技术层面的解决方案，但钓鱼攻击仍是一个严重问题。如今，基于AI的钓鱼检测通过为数据提供风险属性（即钓鱼或良性）预测，成为抵御钓鱼攻击最有效的方案之一。然而，该方法在为预测提供全面解释方面缺乏可解释性，例如无法识别导致数据被归类为钓鱼的具体信息。为此，我们提出了一种创新的基于深度学习的方法，用于对电子邮件（最常见的钓鱼途径）钓鱼攻击进行定位。我们的方法不仅能够预测电子邮件数据的风险属性，还能自动学习并找出钓鱼邮件数据中最重要且与钓鱼相关的信息（即句子），所选信息可为风险属性提供有用且简洁的解释。在7个真实世界的多样化电子邮件数据集上开展的严格实验表明，我们提出的方法在筛选关键信息、为钓鱼邮件数据的风险属性提供简洁解释（通过成功找出最重要且与钓鱼相关的信息）方面具备有效性与先进性。值得注意的是，以Label-Accuracy（标签准确率）和Cognitive-True-Positive（认知真阳性率）两个主要指标的综合平均性能衡量，我们的方法相较于现有最优基线取得了约1.5%至3.5%的显著性能提升。
英文摘要：
Phishing attacks have become a serious and challenging issue for detection, explanation, and defense. Despite more than a decade of research on phishing, encompassing both technical and non-technical remedies, phishing continues to be a serious problem. Nowadays, AI-based phishing detection stands out as one of the most effective solutions for defending against phishing attacks by providing vulnerability (i.e., phishing or benign) predictions for the data. However, it lacks explainability in terms of providing comprehensive interpretations for the predictions, such as identifying the specific information that causes the data to be classified as phishing. To this end, we propose an innovative deep learning-based approach for email (the most common phishing way) phishing attack localization. Our method can not only predict the vulnerability of the email data but also automatically learn and figure out the most important and phishing-relevant information (i.e., sentences) in the phishing email data where the selected information indicates useful and concise explanations for the vulnerability. The rigorous experiments on seven real-world diverse email datasets show the effectiveness and advancement of our proposed method in selecting crucial information, offering concise explanations (by successfully figuring out the most important and phishing-relevant information) for the vulnerability of the phishing email data. Particularly, our method achieves a significantly higher performance, ranging from approximately 1.5% to 3.5%, compared to state-of-the-art baselines, as measured by the combined average performance of two main metrics Label-Accuracy and Cognitive-True-Positive.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
