Apply anomaly grey forecasting algorithm to cyberspace situation prediction

In recent years, much research has been devoted to the cyberspace situation awareness; nevertheless, few have investigated the case that the network traffic data collected may include missing values and sufficient network traffic data may not be acquired for privacy protection or the limitation of network storage equipment capacity. Our focus in this position paper is on introducing an anomaly grey forecasting (AGF) method for cyberspace situation prediction under less data little sample, and the experiment with Abilene network Netflow data verify this method.