Abstract

Flash-crowd attacks are the most vicious form of distributed denial of service (DDoS). They flood the victim with service requests generated from numerous bots. Attack requests are identical in content to those generated by legitimate, human users, and bots send at a low rate to appear non-aggressive - these features defeat many existing DDoS defenses. We propose defenses against flash-crowd attacks via human behavior modeling, which differentiate DDoS bots from human users. Current approaches to human-vs-bot differentiation, such as graphical puzzles, are insufficient and annoying to humans, whereas our defenses are highly transparent. We model three aspects of human behavior: a) request dynamics, by learning several chosen features of human interaction dynamics, and detecting bots that exhibit higher aggressiveness in one or more of these features, b) request semantics, by learning transitional …

Date

June 14, 2009

Authors

Georgios Oikonomou, Jelena Mirkovic

Conference

2009 IEEE International Conference on Communications

Pages

1-6

Publisher

IEEE