Virtual coordinate systems (VCSs) provide accurate estimations of latency between arbitrary hosts on a network, while conducting a small amount of actual measurements and relying on node cooperation. While these systems have good accuracy under benign settings, they suffer a severe decrease of their effectiveness when under attack by compromised nodes acting as insider attackers. Previous defenses mitigate such attacks by using machine learning techniques to differentiate good behavior (learned over time) from bad behavior. However, these defense schemes have been shown to be vulnerable to advanced attacks that make the schemes learn malicious behavior as good behavior. We present Newton, a decentralized VCS that is robust to a wide class of insider attacks. Newton uses an abstraction of a real-life physical system, similar to that of Vivaldi, but in addition uses safety invariants derived from Newton's laws of motion. As a result, Newton does not need to learn good behavior and can tolerate a significantly higher percentage of malicious nodes. We show through simulations and real-world experiments on the PlanetLab testbed that Newton is able to mitigate all known attacks against VCSs while providing better accuracy than Vivaldi, even in benign settings. Finally, we show how to design a VCS that better matches a real physical system, thus allowing for more intuitive and tighter system parameters that are even more difficult to exploit by attackers.

Virtual coordinate systems (VCS) provide accurate estimations of latency between arbitrary hosts on a network, while conducting a small amount of actual measurements and relying on node cooperation. While these systems have good accuracy under benign settings, they suffer a severe decrease of their effectiveness when under attack by compromised nodes acting as insider attackers. Previous defenses mitigate such attacks by using machine learning techniques to differentiate good behavior (learned over time) from bad behavior. However, these defense schemes have been shown to be vulnerable to advanced attacks that make the schemes learn malicious behavior as good behavior. We present Newton, a decentralized VCS that is robust to a wide class of insider attacks. Newton uses an abstraction of a real-life physical system, similar to that of Vivaldi, but in addition uses safety invariants derived from Newton's laws of motion. As a result, Newton does not need to learn good behavior and can tolerate a significantly higher percentage of malicious nodes. We show through simulations and real-world experiments on the Planet Lab test bed that Newton is able to mitigate all known attacks against VCS while providing better accuracy than Vivaldi, even in benign settings.

Virtual coordinate systems provide an accurate and efficient service that allows hosts on the Internet to determine latency to arbitrary hosts based on information provided by a subset of participating nodes. Unfortunately, the accuracy of the service can be severely impacted by compromised nodes providing misleading information. 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 consider attacks that target the latency estimation (inflation, deflation, oscillation) and defense mechanisms that combine outlier detection with control theory to deter adaptive 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.

We propose a game theoretical model for fuzz testing, consisting in generating unexpected input to search for software vulnerabilities. As of today, no performance guarantees or assessment frameworks for fuzzing exist. Our paper addresses these issues and describes a simple model that can be used to assess and identify optimal fuzzing strategies, by leveraging game theory. In this context, payoff functions are obtained using a tainted data analysis and instrumentation of a target application to assess the impact of different fuzzing strategies.

This paper proposes a new model, based on mainstream game theory for the optimal configuration of services. We consider the case of reliable realtime P2P communications and show how the configuration of security mechanisms can be configured using game theoretical concepts, in which the defendant is played by the management plane having to face adversaries which play the attacker role. Our main contribution lies in proposing a risk assessment framework and deriving optimal strategies - in terms of Nash equilibrium - for both the attacker and the defendant. We consider the specific service of communications in autonomic networks and we show how the optimal configuration can be determined within the proposed framework.