We present Netscope, a tomographic technique that infers the loss rates of network links from unicast end-to-end measurements. Netscope uses a novel combination of first- and second-order moments of end-to-end measurements to identify and characterize the links that cannot be (accurately) characterized through existing practical tomographic techniques. Using both analytical and experimental tools, we show that Netscope enables scalable, accurate link-loss inference: in a simulation scenario involving 4000 links, 20% of them lossy, Netscope correctly identifies 94% of the lossy links with a false positive rate of 16%—a significant improvement over the existing alternatives. Netscope is robust in the sense that it requires no parameter tuning, moreover its advantage over the alternatives widens when the number of lossy links increases. We also validate Netscope’s performance on an “Internet tomographer” that we deployed on an overlay of 400 PlanetLab nodes.