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 arbi- trary 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 define and use a game theory framework in order to identify the best attack and defense strategies assuming that the attacker is aware of the defense mechanisms. Our approach leverages concepts derived from the Nash equilibrium to model more powerful adversaries. We apply the game theory framework to demonstrate the impact and efficiency of these attack and defense strategies using a well-known virtual coordinate system and real-life Internet data sets. Thereafter, 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.
