Intelligent Machine Learning Algorithms for Reliable Phishing URL Identification: Review

Phishing attacks continue to pose a significant threat to cybersecurity, with attackers using deceptive techniques to lure unsuspecting users into divulging sensitive information such as login credentials, financial details, and personal data. As the volume and sophistication of phishing attacks increase, there is a growing need for effective detection mechanisms to thwart these malicious activities. Machine learning (ML) has emerged as a promising approach for detecting phishing URLs due to its ability to analyze large datasets and identify patterns indicative of malicious intent. This study presents a comprehensive literature review focusing on the methodologies employed in detecting phishing URLs using ML models. The review encompasses various ML techniques such as supervised learning, unsupervised learning, and deep learning, highlighting their strengths and limitations in the context of phishing URL detection. Additionally, the study explores the challenges faced in this domain, feature extraction techniques, and the dynamic nature of phishing attacks. Furthermore, the study examines the types of features commonly used in ML-based phishing URL detection, such as lexical features (e.g., URL length, domain age), content-based features (e.g., presence of keywords), and structural features (e.g., URL hierarchy). The analysis considers the relevance of these features in differentiating between legitimate and malicious URLs and discusses strategies for feature selection and extraction. This research provides valuable insights into the state-of-the-art methodologies, technologies, features, and datasets in ML-based phishing URL detection