Multi Model Approach for Phishing Website Detection using ML and DL Techniques

Abstract

In the technology era, Phishing has continued to be a great challenge within the cybersecurity and web security landscape. This involves exploiting human trust on any online services and subtle technical flaws. This is to gather credentials, financial data, and sensitive information across diverse online platforms and various users. Traditional defenses like static blacklists, signature-based filters and simple detection rules are limited by slow update cycles and an inability to capture subtle syntactic and behavioral cues. To address these shortcomings, we propose a hybrid detection framework that fuses classical supervised machine-learning classifiers (e.g., Logistic Regression, SVM, Random Forest, XGBoost) with sequence-aware deep learning (LSTM) to jointly model lexical, structural, syntactic, and behavioral features extracted from URLs and webpage metadata. This combined approach leverages the interpretability and stability of ML models alongside the pattern-learning strength of LSTMs to detect both known and zero-day phishing attempts, produce calibrated confidence scores and deliver comprehensive reports via a real-time web interface resulting in a robust, transparent, and operationally useful solution for strengthening web security.