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RateGuard: A Robust Distributed Denial of Service (DDoS) Defense System

机译:Rateguard:一种强大的分布式拒绝服务(DDOS)防御系统

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One of the major threats to cyber security is the Distributed Denial-of-Service (DDoS) attack. In this paper, we focus on three kinds of sophisticated DDoS attacks that seriously cripple the current DDoS defense systems and have not been solved yet. In Fast Adaptive Attacks (FAAs), attackers adaptively generate attacking traffic based on the feedback from a victim in Round Trip Time (RTT). Almost all proposed rules-based filtering schemes cannot effectively defend against FAAs, since they need a relatively long time (compared to RTT) to update filtering rules. In Adaptive Attacks with statistical filtering rules Scanning (AAS), attackers circumvent the defense system by discovering the statistical filtering rules of the defense system and then generating flooding traffic to mimic nominal traffic. In Low-Rate TCP Attacks (LRAs), attackers send periodic attack pulses to overflow a router's buffer and force the legitimate TCP flow to a low throughput while staying under the radar with a very low average rate. In this paper, we propose a Leaky-Bucket (LB) based highly robust DDoS defense system, called RateGuard. It can react to FAAs and LRAs by rate-limiting excessive traffic in real-time according to the victim's nominal traffic profile. Moreover, by associating an LB with each joint attribute value, the huge space required for possible joint attribute values makes it almost impossible for attackers to scan the victim's nominal traffic profiles and, thus, makes it highly robust to cope with AAS and other sophisticated attacks.
机译:网络安全的主要威胁之一是分布式拒绝服务(DDOS)攻击。在本文中,我们专注于三种复杂的DDOS攻击,这严重抓住了当前的DDOS防御系统,尚未解决。在快速自适应攻击(FAAS)中,攻击者基于往返时间(RTT)的受害者的反馈,自适应地生成攻击流量。几乎所有提出的基于规则的过滤方案都无法有效地防御FAAS,因为它们需要相对较长的时间(与RTT)来更新过滤规则。在统计过滤规则扫描的自适应攻击中,攻击者通过发现防御系统的统计过滤规则来绕过防御系统,然后生成泛滥业务以模仿名义流量。在低速率TCP攻击(LRAS)中,攻击者发送周期性攻击脉冲以溢出路由器的缓冲区,并强制在雷达下以非常低的平均速率保持低吞吐量。在本文中,我们提出了一种基于漏洞(LB)的高度强大的DDOS防御系统,称为Rateguard。它可以通过根据受害者的标称流量概况限制过度的交通来对FAAS和LRA作出反应。此外,通过将LB与每个联合属性值相关联,可能的联合属性值所需的巨大空间使攻击者几乎不可能扫描受害者的名义流量概况,因此使其使其对应对AAS和其他复杂的攻击非常强大。

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