期刊名称：International Journal of Network Security & Its Applications
印刷版ISSN：0975-2307
电子版ISSN：0974-9330
出版年度：2019
卷号：11
期号：5
页码：27-34
DOI：10.5121/ijnsa.2019.11503
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Due to the rapid growth of the internet, malicious websites [1] have become the cornerstone for internet crime activities. There are lots of existing approaches to detect benign and malicious websites — some of them giving near 99% accuracy. However, effective and efficient detection of malicious websites has now seemed reasonable enough in terms of accuracy, but in terms of processing speed, it is still considered an enormous and costly task because of their qualities and complexities. In this project, We wanted to implement a classifier that would detect benign and malicious websites using network and application features that are available in a data-set from Kaggle, and we will do that using MapReduce to make the classification speeds faster than the traditional approaches.[2].
