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The escalating reliance on revolutionary online web services has introducedheightened security risks, with persistent challenges posed by phishing despiteextensive security measures. Traditional phishing systems, reliant on machinelearning and manual features, struggle with evolving tactics. Recent advancesin deep learning offer promising avenues for tackling novel phishing challengesand malicious URLs. This paper introduces a two-phase stack generalized modelnamed AntiPhishStack, designed to detect phishing sites. The model leveragesthe learning of URLs and character-level TF-IDF features symmetrically,enhancing its ability to combat emerging phishing threats. In Phase I, featuresare trained on a base machine learning classifier, employing K-foldcross-validation for robust mean prediction. Phase II employs a two-layeredstacked-based LSTM network with five adaptive optimizers for dynamiccompilation, ensuring premier prediction on these features. Additionally, thesymmetrical predictions from both phases are optimized and integrated to traina meta-XGBoost classifier, contributing to a final robust prediction. Thesignificance of this work lies in advancing phishing detection withAntiPhishStack, operating without prior phishing-specific feature knowledge.Experimental validation on two benchmark datasets, comprising benign andphishing or malicious URLs, demonstrates the model's exceptional performance,achieving a notable 96.04% accuracy compared to existing studies. This researchadds value to the ongoing discourse on symmetry and asymmetry in informationsecurity and provides a forward-thinking solution for enhancing networksecurity in the face of evolving cyber threats.

AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection