IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

As the scale of the networks continually expands, the detection of distributed denial of service (DDoS) attacks has become increasingly vital. We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network (DNN). The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible, thereby reducing data volume. Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic, enhancing the neural network's generalization capabilities. Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic. Compared with the benchmark methods, our method reaches 99.9% on low-rate DDoS (LDDoS), flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%, 31% and 8%.