Anomaly detection is a first and important step needed to respond to unexpected problems and to assure high performance and security in IP networks. We introduce a framework and a powerful class of algorithms for network anomography, the problem of inferring network-level anomalies from widely available data aggregates. The framework contains novel algorithms, as well as a recently published approach based on Principal Component Analysis (PCA). Moreover, owing to its clear separation of inference and anomaly detection, the framework opens the door to the creation of whole families of new algorithms. We introduce several such algorithms here, based on ARIMA modeling, the Fourier transform, Wavelets, and Principal Component Analysis. We introduce a new dynamic anomography algorithm, which effectively tracks routing and traffic change, so as to alert with high fidelity on intrinsic changes in network-level traffic, yet not on internal routing changes. An additional benefit of dynamic anomography is that it is robust to missing data, an important operational reality. To the best of our knowledge, this is the first anomography algorithm that can handle routing changes and missing data. To evaluate these algorithms, we used several months of traffic data collected from the Abilene network and from a large Tier-1 ISP network. To compare performance, we use the methodology put forward earlier for the Abilene data set. The findings are encouraging. Among the new algorithms introduced here, we see: high accuracy in detection (few false negatives and few false positives), and high robustness (little performance degradation in the presence of measurement noise, missing data and routing changes).