The modeling of realistic networks is of great importance for complex systemsresearch. Previous procedures typically model the natural growth of networks byiteratively adding nodes, use geometric positioning information, define linkconnectivity with preference for nearest neighbors or already highly connectednodes, or combine several of these approaches. Our novel model is based on the well-know concept of 'k'-cores, originally introduced in social network analysis. Recent studies exposed the significant 'k'-core structure of several real world systems, e.g. the AS network of theInternet. We present two algorithms for generating networks which strictlyadhere to the sizes of a given 'k'-core structure but also exhibit adaptationto various use cases. We showcase this in a comparative evaluation with twowell-known AS network generators.