Algorithm 3: K-means clustering

Input: K (the number of clusters); K = 2 (malicious or benign) D dataset contains best feature values $f_{best,i}$ for each URLi Output: $Cluster S_j$ Begin Arbitrary choose K objects from D as the initial cluster centers; Repeat -(re) assign each object to the cluster to which the object is the most similar, based on the mean value of the objects in the cluster; -Update the cluster means, i.e., calculate the mean value of the objects for each cluster Until no change;