Malicious Uniform Resource Locator/malicious websites are a high threat to cyber security. Malicious URLs host
uninvited content like malware, spam, drive by downloads phishing, etc. Users become victims of scams like financial loss,
thieving of personal data, malware installation, and causes losses of millions of dollars every year. There is a need to detect those
threats in a very efficient and timely manner. Several studies have examined different techniques to handle the problem; the
foremost used approach remains blacklisting. The most obstacle to using blacklist is that the difficulties in maintaining an up-todate list of URLs. So here we proposed the Machine learning approach to detect the malicious URLs. We also discussed various
methods for malicious URL detection, feature representations, and finally discussed various algorithms for the classification and
feature extraction.
