Booter blacklist generation based on content characteristics

Abstract

Distributed Denial of Service (DDoS) attacks-as-a-service, known as Booter or Stresser, is convenient and low-priced for ordinary people to launch DDoS attacks. It makes DDoS attacks even more rampant. However, until now there is not much research on Booter and little acquaintance with their backend infrastructure, customers, business, etc. In this paper, we present a new method which focuses on the content (text) characteristics on Booters websites and selects more discriminative features between Booter and non-Booter to identify Booters more effectively in the Internet. The experimental results show that the classification accuracy of distinguishing Booter and non-Booter websites is 98.74%. In addition, our method is compared with several representative methods and the results show that the proposed method outperforms the classical methods in 66% of the classification cases on three datasets: Booter websites, 20-Newsgroups and WebKB.