The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model's exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

翻译：随着对革命性在线网络服务的依赖日益增加，安全风险也随之上升，尽管采取了广泛的安全措施，钓鱼攻击仍持续带来挑战。传统依赖于机器学习和手动特征的钓鱼检测系统难以应对不断演变的攻击手法。深度学习的近期进展为处理新型钓鱼挑战和恶意URL提供了有前景的途径。本文提出了一种名为AntiPhishStack的两阶段堆叠泛化模型，旨在检测钓鱼网站。该模型对称地利用URL和字符级TF-IDF特征的学习能力，增强了应对新兴钓鱼威胁的效果。在第一阶段，特征在基础机器学习分类器上进行训练，采用K折交叉验证以获得稳健的平均预测。第二阶段采用双层堆叠LSTM网络，配备五种自适应优化器进行动态编译，确保对这些特征的最优预测。此外，两个阶段的对称预测经过优化并整合，用于训练元XGBoost分类器，从而形成最终稳健的预测。本研究的意义在于通过AntiPhishStack推进钓鱼检测，且无需依赖先验的钓鱼特定特征知识。在两个包含良性及钓鱼或恶意URL的基准数据集上的实验验证表明，该模型性能卓越，与现有研究相比达到了96.04%的显著准确率。这项研究为信息安全的对称性与非对称性讨论增添了价值，并为在日益演变的网络威胁中增强网络安全提供了前瞻性解决方案。