Abstract. In this paper, some clustering techniques are analyzed to compare their ability to detect botnet traffic by selecting features that distinguish connections belonging to or not belonging to a botnet. By considering the history of network's connections, some clustering algorithms are used to derive a set of rules to decide which should be considered as a botnet. Our main contribution is to evaluate different clustering techniques to detect botnets based on their detection rate (true and false positives). The algorithms used are K-medoids and K-means clustering. Datasets used in this paper were extracted from the repositories ISOT and ISCX. Results on K-medoids were better for almost all the experiments than K-means.
Francisco Villegas Alejandre, Nareli Cruz Cortés, Eleazar Aguirre Anaya, 2016 REF compared some clustering techniques on their ability to detect botnet traffic by selecting features that distinguish connections belonging to or not belonging to a botnet.
Botnet Detection using Clustering Algorithms
Res. Comput. Sci.
Res. Comput. Sci.
Computer Science