their practical value when requiring real-time security assurance compared to blacklisting methods.

In this survey, we review the state-of-the-art machine learning techniques for malicious URL detection in literature. We specifically focus on the contributions made for feature representation and learning algorithm development in this domain. We systematically categorize the various types of feature representation used for creating the training data for this task, and also categorize various learning algorithms used to learn a good prediction model. We also discuss the open research problems and identify directions for future research. In the rest of the survey, we first discuss the broad categories of strategies used for detecting malicious URLs - Blacklists, Heuristic and Machine Learning. We formalize the setting as a machine learning problem, where the primary requirement is good feature representation and the learning algorithm used. We then comprehensively present various types of feature representation used for this problem. This is followed by presenting various algorithms that have been used to solve this task, and have been developed based on the properties of URL data. Finally we discuss the newly emerging concept of Malicious URL Detection as a service and the principles to be used while designing such a system. 