Inferring distributed reflection denial of service attacks from darknet

This work proposes a novel approach to infer and characterize Internet-scale DNS Distributed Reflection Denial of Service (DRDoS) attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) activities using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DRDoS activities such as intensity, rate and geo-location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks and the expectation maximization and k-means clustering techniques in an attempt to identify campaigns of DRDoS Attacks. We empirically evaluate the proposed approach using 1.44 TB of real darknet data collected from a/13 address space during a recent several months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DRDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The extracted insights from various validated DNS DRDoS case studies lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DRDoS activities.