Efficient identification of bots by K-means clustering

Abstract

Botnet has become a major threat to the Internet and has gained a lot of attention from cyber security. Attackers have been increasing on the Internet in order to gain profits by stealing the information of a legitimate user. As per the statistics of Vintcerf, “25 % of internets PCs are part of a botnet”. Bots are termed as a collection of compromised computers responsible for various attacks such as phishing attack, DDOS attack, spam e-mails, online fraud, phishing, information exfiltration, etc. In this paper, traditional botminer algorithm is used with k-means clustering. In addition, x-means clustering is used to cluster the traffic and the existing algorithm was executed to compare and validate the results and differences revealed by k-means and x-means clustering algorithms