LAUSR

Abstract Recent advances in data science have made available many URL-analysis–based detection and machine learning algorithms, which are being leveraged for phishing detection. It is necessary to measure the efficacy of each technique so as to select an efficient phishing detection technique for integrating into a system. There have been a number of studies stated so far in this related field to assist with this task. While most of the studies have covered all the approaches, only a few have concentrated their study on machine learning–based techniques. Additionally, the parameters such as true-positive, false-positive, true-negative, and false-negative rates have been analyzed in most of the studies in these related articles. In order to achieve new objectives in the field of security, the study on analysis of phishing detection techniques needs to be expanded. In contrast to state-of-the-art methods, our research scrutinizes the different mechanisms and taxonomy used in each machine learning–based detection technique succeeded by an upper bound computing time.