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Open Access

ARTICLE

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

Yanhua Liu1,2,3, Yuting Han1,2,3, Hui Chen1,2,3, Baokang Zhao4,*, Xiaofeng Wang4, Ximeng Liu1,2,3
1 College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
2 Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou, 350108, China
3 Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350108, China
4 College of Computer, National University of Defense Technology, Changsha, 410073, China
* Corresponding Author: Baokang Zhao. Email: email
(This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.051194

Received 29 February 2024; Accepted 11 June 2024; Published online 15 July 2024

Abstract

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%.

Keywords

DDoS; real-time; improved generalized entropy; DNN

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