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