Elastic Detection Mechanism Aimed at Hybrid DDoS Attack

Abstract

In Distributed Denial of Service(DDoS) attack, the attacker uses a remotely controlled botnet to attack the target server at the same time to prevent legitimate users from obtaining information services. Previous studies focused on the detection of DDoS attacks on offline datasets, but ignored the detection of specific DDoS types, and some reports showed that the number of DDoS hybrid attacks was increasing significantly. In this paper, we propose an elastic detection mechanism(EDM), which can economize the server’s idle computing power. The framework integrates a variety of pre-trained lightweight CNN detect models, which are suitable for online rapid detection of DDoS hybrid attacks. We focus on evaluating the response accuracy and the detection speed of the EDM. The experimental results show that the model can achieve excellent hybrid attack detection performance, and meet the actual requirements of real-time detection.