TY - JOUR
T1 - Booter list generation
T2 - The basis for investigating DDoS-for-hire websites
AU - Santanna, José Jair
AU - de Vries, Joey
AU - de O. Schmidt, Ricardo
AU - Tuncer, Daphne
AU - Z. Granville, Lisandro
AU - Pras, Aiko
N1 - Special Issue: Security for Emerging Open Networking Technologies
PY - 2018/1/1
Y1 - 2018/1/1
N2 - The expansion of Distributed Denial of Service (DDoS)–for-hire websites, known as Booters, has radically modified both the scope and stakes of DDoS attacks. Until recently, however, Booters have only received little attention from the research community. Given their impact, addressing the challenges associated with this phenomenon is crucial. In this paper, we present a rigorous methodology to identify a comprehensive set of existing Booters in the Internet. Before presenting our methodology, we illustrate the benefits of a set of booters on monitoring users from the Dutch NREN, SURFNet, from 2015 to 2017. Our methodology relies on well-defined mechanisms to generate a Booter list, from crawling suspect URLs to characterizing and classifying the collected URLs. The list obtained using the methodology presented in this paper has a classification accuracy of 95.5%, which is 10.5% better compared to previous work.
AB - The expansion of Distributed Denial of Service (DDoS)–for-hire websites, known as Booters, has radically modified both the scope and stakes of DDoS attacks. Until recently, however, Booters have only received little attention from the research community. Given their impact, addressing the challenges associated with this phenomenon is crucial. In this paper, we present a rigorous methodology to identify a comprehensive set of existing Booters in the Internet. Before presenting our methodology, we illustrate the benefits of a set of booters on monitoring users from the Dutch NREN, SURFNet, from 2015 to 2017. Our methodology relies on well-defined mechanisms to generate a Booter list, from crawling suspect URLs to characterizing and classifying the collected URLs. The list obtained using the methodology presented in this paper has a classification accuracy of 95.5%, which is 10.5% better compared to previous work.
KW - 2019 OA procedure
UR - http://www.scopus.com/inward/record.url?scp=85040788655&partnerID=8YFLogxK
U2 - 10.1002/nem.2008
DO - 10.1002/nem.2008
M3 - Special issue
AN - SCOPUS:85040788655
SN - 1055-7148
VL - 28
JO - International journal of network management
JF - International journal of network management
IS - 1
M1 - e2008
ER -