  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.
