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首页> 外文期刊>IEEE transactions on dependable and secure computing >A Data-Driven Study of DDoS Attacks and Their Dynamics
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A Data-Driven Study of DDoS Attacks and Their Dynamics

机译：DDOS攻击及其动态的数据驱动研究

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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.
机译：尽管持续努力，但互联网仍然普遍普遍存在。在这种武器比赛中，攻击者变得更加敏捷，他们的策略更复杂，以逃离检测。有效的防御需求深入了解此类策略。在本文中，我们将从攻击者的角度调查DDOS景观。我们专注于攻击力的动态，旨在探索场景背后的策略，如果有的话。我们的研究基于全球的50,704个不同的互联网DDOS攻击，在七个月内。我们的结果表明，攻击者故意以动态方式对其受控机器人进行安排，并且可以通过统计分布充分捕获这种动态。此外，不同的僵尸网络系列表现出类似的调度模式，强烈建议他们的密切关系和潜在的合作。通过在多个家族中旋转的机器人进一步证实了这种合作，并且在各种水平上检查并确认这种旋转模式。这些调查结果为预测未来的DDOS袭击进行了一个很有希望的基础，并援助缓解努力。

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