Stealthy DGoS Attack under Passive and Active Measurements

As a tool to infer the internal state of a network that cannot be measured directly (e.g., the Internet and all-optical networks), network tomography has been extensively studied under the assumption that the measurements truthfully reflect the end-to-end performance of measurement paths, which makes the resulting solutions vulnerable to manipulated measurements. In this work, we investigate the impact of manipulated measurements via a recently proposed attack model called the stealthy DeGrading of Service (DGoS) attack, which aims at maximally degrading path performances without exposing the manipulated links to network tomography. While existing studies on this attack assume that network tomography only measures the paths actively used for data transfer (by passively recording the performance of data packets), our model allows network tomography to measure a larger set of paths, e.g., by sending probes on some paths not carrying data flows. By developing and analyzing the optimal attack strategy, we quantify the maximum damage of such an attack and shed light on possible defenses.