POSTER: Cyber Attack Prediction of Threats from Unconventional Resources (CAPTURE)

This paper outlines the design, implementation and evaluation of CAPTURE - a novel automated, continuously working cyber attack forecast system. It uses a broad range of unconventional signals from various public and private data sources and a set of signals forecasted via the Auto-Regressive Integrated Moving Average (ARIMA) model. While generating signals, auto cross correlation is used to find out the optimum signal aggregation and lead times. Generated signals are used to train a Bayesian classifier against the ground truth of each attack type. We show that it is possible to forecast future cyber incidents using CAPTURE and the consideration of the lead time could improve forecast performance.