A Data-Driven Study of DDoS Attacks and Their Dynamics

Abstract

Despite continuous defense efforts, DDoS attacks are still very prevalent on the Internet. In such arms races, attackers are becoming more agile and their strategies are more sophisticated to escape from detection. Effective defenses demand in-depth understanding of such strategies. In this paper, we set to investigate the DDoS landscape from the perspective of the attackers. We focus on the dynamics of the attacking force, aiming to explore the strategies behind the scenes, if any. Our study is based on 50,704 different Internet DDoS attacks across the globe in a seven-month period. Our results indicate that attackers deliberately schedule their controlled bots in a dynamic fashion, and such dynamics can be well captured by statistical distributions. Furthermore, different botnet families exhibit similar scheduling patterns, strongly suggesting their close relationship and potential collaborations. Such collaborations are further confirmed by bots rotating in multiple families, and such rotation patterns are examined and confirmed at various levels. These findings lay a promising foundation for predicting DDoS attacks in the future and aid mitigation efforts.