An Identification and Analysis of Harmful URLs through the Application of Machine Learning Techniques - Karve, Swagat M.; Kakad , Shital; Swapnaja Amol; Gavali, Ashwini B.; Gavali , Sonali B.; Shirkande, Shrinivas T. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Malicious URLs pose a significant cyber security threat, posing risks to user security and causing substantial financial losses. Traditional detection methods relying on blacklists are limited in addressing rapidly evolving threats. As a response, machine learning approaches have gained popularity for enhancing the efficiency of malicious URL detection. This paper presents a detailed analysis, offering a structured insight into various aspects and formally defining the machine learning task of identifying malicious URLs. It delves into feature representation, algorithm design. The objective of survey is to provide a detailed analysis of harmful URLS not only to researchers but to cyber security experts.