A Systematic Review of Evaluating the Efficiency of Hybrid Machine Learning Techniques in Unmasking Phishing URLs

Abstract

Phishing URLs pose significant threats to individuals and organizations, exploiting vulnerabilities to perpetrate scams and fraud. The Indian Cybercrime Coordination Center (I4C) faces challenges in effectively addressing these threats, necessitating a systematic examination of phishing URL detection methods. This review article focuses on evaluating the efficacy of hybrid machine learning algorithms, particularly URL-based techniques, in combating phishing. Leveraging the unparalleled accuracy and performance of hybrid machine learning models, this research represents a groundbreaking approach to early detection and mitigation of phishing URLs, which are a prevalent cause of fraud and hacking globally. Recent advancements in hybrid machine learning have facilitated the integration of multiple methods to enhance accuracy and reliability in identifying and thwarting phishing attempts. This study contributes to the ongoing efforts in cybersecurity by shedding light on the potential of hybrid machine learning techniques in unmasking phishing URLs, thereby bolstering defenses against cyber threats. Key Words: Phishing URLs, Hybrid Machine Learning, accuracy, URL-based techniques, Indian cybercrime coordination center (I4C)