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

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

Dr. Pradip Paithane1, Siddheshwar Patil2, Abhishek Kubde3,

Rohit Mehetre4, Shubham Gaikwad5

1Department of Artificial Intelligence and Data Science, VPKBIET, Baramati

2Department of Artificial Intelligence and Data Science, VPKBIET, Baramati

3Department of Artificial Intelligence and Data Science, VPKBIET, Baramati

4Department of Artificial Intelligence and Data Science, VPKBIET, Baramati

5Department of Artificial Intelligence and Data Science, VPKBIET, Baramati

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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)