Securing Application-Level Topology Estimation Networks: Facing the Frog-Boiling Attack
Project Members
Jeff Seibert, Cristina Nita-Rotaru, Radu State
Jeff Seibert, Cristina Nita-Rotaru, Radu State
Abstract
Peer-to-peer real-time communication and media streaming applications optimize their performance by using application-level topology estimation services such
as virtual coordinate systems. Virtual coordinate systems allow nodes in
a peer-to-peer network to accurately predict latency between arbitrary nodes
without the need of performing extensive measurements. However, systems that leverage virtual coordinates as supporting building blocks, are prone to attacks conducted by compromised nodes that aim at disrupting, eavesdropping,
or mangling with the underlying communications.
Recent research proposed techniques to mitigate basic attacks (inflation,
deflation, oscillation) considering a single attack strategy model where
attackers perform only one type of attack. In this work we explore supervised
machine learning techniques to mitigate more subtle yet highly effective
attacks (frog-boiling, network-partition) that are able to bypass
existing defenses. We evaluate our techniques on the Vivaldi system against a more complex attack
strategy model, where attackers perform sequences of all known attacks against
virtual coordinate systems, using both simulations and Internet deployments.