Visible to the public Biblio

Filters: Author is Retvari, Gabor  [Clear All Filters]
2019-02-08
Csikor, Levente, Rothenberg, Christian, Pezaros, Dimitrios P., Schmid, Stefan, Toka, László, Retvari, Gabor.  2018.  Policy Injection: A Cloud Dataplane DoS Attack. Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos. :147-149.

Enterprises continue to migrate their services to the cloud on a massive scale, but the increasing attack surface has become a natural target for malevolent actors. We show policy injection, a novel algorithmic complexity attack that enables a tenant to add specially tailored ACLs into the data center fabric to mount a denial-of-service attack through exploiting the built-in security mechanisms of the cloud management systems (CMS). Our insight is that certain ACLs, when fed with special covert packets by an attacker, may be very difficult to evaluate, leading to an exhaustion of cloud resources. We show how a tenant can inject seemingly harmless ACLs into the cloud data plane to abuse an algorithmic deficiency in the most popular cloud hypervisor switch, Open vSwitch, and reduce its effective peak performance by 80–90%, and, in certain cases, denying network access altogether.

2017-03-07
Nikolenko, Sergey I., Kogan, Kirill, Retvari, Gabor, Berczi-Kovacs, Erika R., Shalimov, Alexander.  2016.  How to represent IPv6 forwarding tables on IPv4 or MPLS dataplanes. :521–526.

The Internet routing ecosystem is facing substantial scalability challenges on the data plane. Various “clean slate” architectures for representing forwarding tables (FIBs), such as IPv6, introduce additional constraints on efficient implementations from both lookup time and memory footprint perspectives due to significant classification width. In this work, we propose an abstraction layer able to represent IPv6 FIBs on existing IP and even MPLS infrastructure. Feasibility of the proposed representations is confirmed by an extensive simulation study on real IPv6 forwarding tables, including low-level experimental performance evaluation.