  Distributed Denial of Service (DDoS) is a common type of Cybercrime. It can
strongly damage a company reputation and increase its costs. Attackers improve
continuously their strategies. They doubled the amount of unleashed
communication requests in volume, size, and frequency in the last few years.
This occurs against different hosts, causing resource exhaustion. Previous
studies focused on detecting or mitigating ongoing DDoS attacks. Yet,
addressing DDoS attacks when they are already in place may be too late. In this
article, we consider network resilience by early prediction of attack trends.
We show empirically the advantage of using non-parametric leading indicators
for early prediction of volumetric DDoS attacks. We report promising results
over a real dataset from CAIDA. Our results raise new questions and
opportunities for further research in early predicting trends of DDoS attacks.
