To protect complex power-grid control networks, efficient security assessment techniques are required. However, efficiently making sure that calculated security measures match the expert knowledge is a challenging endeavor. In this paper, we present EliMet, a framework that combines information from different sources and estimates the extent to which a control network meets its security objective. Initially, during an offline phase, a state-based model of the network is generated, and security-level of each state is measured using a generic and easy-to-compute metric. EliMet then passively observes system operators' online reactive behavior against security incidents, and accordingly refines the calculated security measure values. Finally, to make the values comply with the expert knowledge, EliMet actively queries operators regarding those states for which sufficient information was not gained during the passive observation. Our experimental results show that EliMet can optimally make use of prior knowledge as well as automated inference techniques to minimize human involvement and efficiently deduce the expert knowledge regarding individual states of that particular system.