An Intelligent System for DDoS Attack Prediction Based on Early Warning Signals

Abstract

Among the different threats causing significant losses in cyberspace, the distributed denial of service (DDoS) attack is one of the most dangerous. The literature shows that the most reasonable manner to reduce the impacts of a DDoS attack is to prevent an attacker from launching it. Prevention is essential because attack sophistication allows them to reach massive traffic volumes, bypassing defenses. Defense mechanisms need time to detect and mitigate attacks. Hence, it is paramount to manage signals of the attack preparation before the attacker effectively launches it. This work presents COOPRED DDoS, a cooperative system for predicting DDoS attacks based on early warning signals extracted from the preparation of DDoS attacks. Its goal lies in increasing the time to prevent DDoS attacks. This work has followed four experiments utilizing two datasets widely employed in the literature. The results show that COOPRED DDoS identifies signals of attacks before the attacker effectively launches them. The system predicts one of the investigated attacks up to 3 minutes and 49 seconds in advance and the other attack up to 3 minutes and 55 seconds. The accuracy of the experiments varies from 99.60% to 99.87%.