Abstract
Data transfer infrastructures composed of Data Transfer Nodes (DTN) are critical to meeting distributed computing and storage demands of clouds, data repositories, and complexes of supercomputers and instruments. The infrastructure’s throughput profile, estimated as a function of the connection round trip time using Machine Learning (ML) methods, is an indicator of its operational state, and has been utilized for monitoring, diagnosis and optimization purposes. We show that the inherent statistical variations and precision of throughput profiles estimated by ML methods can be exploited for unauthorized use of DTNs’ computing and network capacity. We present a game theoretic formulation that captures the cost-benefit trade-offs between an attacker that attempts to hide under the profile’s statistical variations and a provider that attempts to balance compromise detection with the cost of throughput measurements. The Nash equilibrium conditions adapted to this game provide qualitative insights and bounds for the success probabilities of the attacker and provider, by utilizing the generalization equation of ML-estimate. We present experimental results that illustrate this game wherein a significant portion of DTN computing capacity is compromised without being detected by an attacker that exploits the ML estimate properties.
