A Comparison of Machine Learning Attributes for Detecting Malicious Websites

Authors:
Publisher website
Google Scholar
Abstract
The number of Malicious Websites has increased manifold in the past few years. As on start of year 2018, 1 in every 13 URL was malicious, amounting to 7.8% URLs identified as malicious [1]. These figures have increased by 2.8%, thereby showing an increasing trend of attack vectors through Malicious Websites. These statistics clearly highlight the need to detect Malicious Websites on the Internet. Many research works have suggested Machine Learning techniques to detect Malicious Websites. Research has also been done to compare Machine Learning algorithms for their detection. However, the aspect of attribute selection for detecting Malicious Websites using Machine Learning has not been delved in detail. In Machine Learning techniques, attribute selection outweighs the importance of any other aspect in the process. Thus, there is a need to compare and analyze the various attributes that can help find Malicious Websites faster and better. This paper is focused to address this research gap, so that, fewer and optimal attributes can do a better job.

This publication has 11 references indexed in Scilit: