Minoo Rouhi | Searcharxiv

arXiv2020

Me Love (SYN-)Cookies: SYN Flood Mitigation in Programmable Data Planes

The SYN flood attack is a common attack strategy on the Internet, which tries to overload services with requests leading to a Denial-of-Service (DoS). Highly asymmetric costs for connection setup - putting the main burden on the attackee - make SYN flooding an efficient and popular DoS attack strategy. Abusing the widely used TCP as an attack vector complicates the detection of malicious traffic and its prevention utilizing naive connection blocking strategies. Modern programmable data plane devices are capable of handling traffic in the 10 Gbit/s range without overloading. We discuss how we can harness their performance to defend entire networks against SYN flood attacks. Therefore, we analyze different defense strategies, SYN authentication and SYN cookie, and discuss implementation difficulties when ported to different target data planes: software, network processors, and FPGAs. We provide prototype implementations and performance figures for all three platforms. Further, we fully disclose the artifacts leading to the experiments described in this work.

cs.NI↗

arXiv2017

Large-Scale Classification of IPv6-IPv4 Siblings with Variable Clock Skew

Linking the growing IPv6 deployment to existing IPv4 addresses is an interesting field of research, be it for network forensics, structural analysis, or reconnaissance. In this work, we focus on classifying pairs of server IPv6 and IPv4 addresses as siblings, i.e., running on the same machine. Our methodology leverages active measurements of TCP timestamps and other network characteristics, which we measure against a diverse ground truth of 682 hosts. We define and extract a set of features, including estimation of variable (opposed to constant) remote clock skew. On these features, we train a manually crafted algorithm as well as a machine-learned decision tree. By conducting several measurement runs and training in cross-validation rounds, we aim to create models that generalize well and do not overfit our training data. We find both models to exceed 99% precision in train and test performance. We validate scalability by classifying 149k siblings in a large-scale measurement of 371k sibling candidates. We argue that this methodology, thoroughly cross-validated and likely to generalize well, can aid comparative studies of IPv6 and IPv4 behavior in the Internet. Striving for applicability and replicability, we release ready-to-use source code and raw data from our study.

cs.NI↗