An autonomous router-based solution to detect and defend low rate DDoS attacks

Internet security was not a concern when the Internet was invented, but we cannot deny this fact anymore. Since all forms of businesses and communications are aligned to the Internet in one form or the other, the security of these assets (both infrastructure and content) is of prime importance. Some of the well known consequences of an attack include gaining access to a network, intellectual property thefts, and denial of service. This thesis focuses on countering flood-type attacks that result in denial of service to end users. A new classification of this denial of service attacks, known as the low rate denial of service, will be the crux of our discussion. The average rate of this attack is so low that most routers or victims fail to detect the attack. Thus far, no solution can counter the low rate attacks without degrading the normal performance of the Transmission Control Protocol. This work proposes a router-based solution to detect and defend low as well as high rate distributed denial of service attacks (DDoS). A per flow approach coupled with the Deterministic Packet Marking scheme is used to detect and block attack flows autonomously. The solution provides a rapid detection and recovery procedure during an attack

Low rate TCP denial-of-service attack detection at edge routers,”

Abstract-Low rate TCP Denial-of-Service attacks are a new type of DoS attacks that are carefully orchestrated to exploit the fixed minimum TCP RTO property, and thereby deny services to legitimate users. This type of attacks is different from traditional flood-based attacks, and hence conventional solutions to detect these attacks are not applicable. We propose a novel approach to detect these attack flows at edge routers. A flow exhibiting a periodic pattern is marked malicious if its burst length is greater than or equal to RTTs of other connections with the same server, and its time period is equal to the fixed minimum RTO. A carefully designed light weight data structure is proposed to store the necessary flow history at edge routers. Simulation results show that such flows can be detected by our proposed approach, which does not require any modification to TCP congestion control algorithms like randomizing the fixed minimum RTO