Abstract
Great success has been witnessed in the last few years for approaches combining Machine Learning (ML) with Knowledge Representation and Reasoning (KRR) to predict cybersecurity events. These approaches benefited from the high accuracy of ML, and the inherent transparency of KRR. In this paper, we develop a multi-layered, hybrid system that benefits from both approaches. When the developed system is fused with an existing statistical forecasting model, it demonstrates an average recall improvement of more than 14% while maintaining precision.