![]() ![]() ![]() 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. The experimental results show that the classification accuracy of distinguishing Booter and non-Booter websites is 98.74%. 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. Booter sites, also known as 'stresser' services allow individuals without any. However, until now there is not much research on Booter and little acquaintance with their backend infrastructure, customers, business, etc. On Thursday, federal authorities seized 15 different 'booter' websites and charged three individuals with crimes. 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. ![]()
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