Phishing Web Page Detection Based on Data Enhancement and Feature Engineering

Phishing, a deceptive cyberattack technique, poses a significant threat to online users by tricking them into disclosing personal or financial information through counterfeit web pages that impersonate legitimate ones. This paper explores data enhancement and feature engineering techniques as a means to improve the performance of phishing web page detection methods. Data enhancement involves methods like data augmentation, oversampling, and undersampling, which aim to balance class distribution and enhance data diversity and quality. These techniques are essential to mitigate the data imbalance issue often encountered in phishing detection datasets. Feature engineering techniques, including feature selection, extraction, and transformation, are introduced to reduce data dimensionality and enhance the discriminative power of features. The proposed approach is experimentally validated, providing a valuable resource for improving online security in the face of evolving cyber threats. This work lays the foundation for future research in the domain of phishing detection, addressing a critical issue in cybersecurity.