Moving-Target Defense Against Botnet Reconnaissance and an Adversarial Coupon-Collection Model

Title	Moving-Target Defense Against Botnet Reconnaissance and an Adversarial Coupon-Collection Model
Publication Type	Conference Paper
Year of Publication	2018
Authors	Fleck, Daniel, Stavrou, Angelos, Kesidis, George, Nasiriani, Neda, Shan, Yuquan, Konstantopoulos, Takis
Conference Name	2018 IEEE Conference on Dependable and Secure Computing (DSC)
ISBN Number	978-1-5386-5790-4
Keywords	adversarial coupon collection, adversarial coupon-collector mathematical model, AWS prototype, Botnet, botnet reconnaissance, cloud based multiserver system, cloud computing, Computational modeling, Computer crime, computer network security, DDoS Attack, DDoS attacker reconnaissance phase, extrapolation, extrapolations, HTTP redirection, hypermedia, Internet, IP networks, motag technique, Moving-Target Defense, Network reconnaissance, numerical evaluations, proactive moving-target defense technique, proxy servers, pubcrawl, Reconnaissance, replica application servers, resilience, Resiliency, Scalability, Servers, Streaming media, transport protocols
Abstract	We consider a cloud based multiserver system consisting of a set of replica application servers behind a set of proxy (indirection) servers which interact directly with clients over the Internet. We study a proactive moving-target defense to thwart a DDoS attacker's reconnaissance phase and consequently reduce the attack's impact. The defense is effectively a moving-target (motag) technique in which the proxies dynamically change. The system is evaluated using an AWS prototype of HTTP redirection and by numerical evaluations of an "adversarial" coupon-collector mathematical model, the latter allowing larger-scale extrapolations.
URL	https://ieeexplore.ieee.org/document/8625162
DOI	10.1109/DESEC.2018.8625162
Citation Key	fleck_moving-target_2018