A Framework for Mitigating Attacks Against Measurement-Based Adaptation Mechanisms in Unstructured Multicast Overlay Networks

Abstract

Many multicast overlay networks maintain application-specific performance goals by dynamically adapting the overlay structure when the monitored performance becomes inadequate. This adaptation results in an unstructured overlay where no neighbor selection constraints are imposed. Although such networks provide resilience to benign failures, they are susceptible to attacks conducted by adversaries that compromise overlay nodes. Previous defense solutions proposed to address attacks against overlay networks rely on strong organizational constraints and are not effective for unstructured overlays. In this work, we identify, demonstrate and mitigate insider attacks against measurement-based adaptation mechanisms in unstructured multicast overlay networks. We propose techniques to decrease the number of incorrect adaptations by using outlier detection and limit the impact of malicious nodes by aggregating local information to derive global reputation for each node. We demonstrate the attacks and mitigation techniques through real-life deployments of a mature overlay multicast system.