Game Strategies for Data Transfer Infrastructures Against ML-Profile Exploits

Abstract

<p>Data transfer infrastructures composed of Data Transfer Nodes (DTN) are critical to supporting distributed computing and storage capabilities for 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 the 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 counters by using additional measurements at an added cost.</p> <p>The Nash equilibrium conditions of this game provide qualitative insights and bounds for the success probabilities of the attacker and provider, based on the generalization equation of the ML-estimate. We present experimental results that illustrate scenarios wherein a significant portion of DTN computing capacity is compromised without being detected by an attacker that exploits the ML estimate properties.</p>