ENHANCING PHISHING DETECTION ACCURACY THROUGH MULTI-OBJECTIVE FEATURE OPTIMIZATION AND XGBOOST

Abstract

The rapid proliferation of malicious Uniform Resource Locators (URLs) presents a serious cybersecurity threat, enabling phishing attacks, malware dissemination, and financial fraud. Conventional detection mechanisms, such as blacklisting and rule-based systems, are largely reactive and struggle to keep pace with dynamically generated and zero-day malicious URLs. Blacklisting methods fail to identify newly created threats, while rule-based approaches rely on predefined heuristics that are ineffective against evolving attack patterns. To address these limitations, this research proposes a multi-objective feature selection and XGBoost-based phishing detection framework aimed at improving classification accuracy and detection robustness. A comprehensive dataset containing both benign and malicious URLs is constructed, followed by extensive feature extraction encompassing lexical, host-based, and network-level attributes. A multi-objective optimization strategy is employed to select the most informative feature subset, reducing redundancy while enhancing model performance. The optimized feature set is then utilized to train an Extreme Gradient Boosting (XGBoost) classifier, which effectively captures complex nonlinear patterns associated with phishing URLs. Experimental evaluation demonstrates improved detection accuracy and generalization capability, enabling proactive identification of previously unseen malicious URLs. The proposed approach offers a scalable, adaptive, and efficient solution for modern phishing detection systems.