id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_spv2h432ejh57fu6rif6mlqq34	H.B. Kazemian	Comparisons of machine learning techniques for detecting malicious webpages	2015	12	.pdf	application/pdf	8347	945	64	This paper compares machine learning techniques for detecting malicious webpages. Black list is a list of webpages which are classified as malicious from a user's point of Please note that K-Means and Affinity Propagation have not been applied to detection of malicious webpages by other researchers. as content, URL and screenshot of webpages were extracted to feed into the machine learning models. utilized machine learning to detect malicious webpages. detect malicious webpages from URL features. supervised machine learning techniques such as Naive Bayes Classifier, K-Nearest Neighbor and Support Vector Machine, and two Results of comparisons of supervised machine learning models that detect malicious malicious to safe webpages is the same in testing as well as training for the supervised machine learning models. Visual representation of unsupervised learning models demonstrating clear separations of malicious and safe webpages. Comparisons of machine learning techniques for detecting malicious webpages Comparisons of machine learning techniques for detecting malicious webpages	./cache/work_spv2h432ejh57fu6rif6mlqq34.pdf	./txt/work_spv2h432ejh57fu6rif6mlqq34.txt