Open Access

ARTICLE

DoS Attack Detection Based on Deep Factorization Machine in SDN

by Jing Wang¹, Xiangyu Lei¹, Qisheng Jiang¹, Osama Alfarraj², Amr Tolba², Gwang-jun Kim^3,*

1 School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha, 410114, China
2 Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia
3 Department of Computer Engineering, Chonnam National University, Gwangju, 61186, Korea

* Corresponding Author: Gwang-jun Kim. Email:

Computer Systems Science and Engineering 2023, 45(2), 1727-1742. https://doi.org/10.32604/csse.2023.030183

Received 20 March 2022; Accepted 04 May 2022; Issue published 03 November 2022

Abstract

Software-Defined Network (SDN) decouples the control plane of network devices from the data plane. While alleviating the problems presented in traditional network architectures, it also brings potential security risks, particularly network Denial-of-Service (DoS) attacks. While many research efforts have been devoted to identifying new features for DoS attack detection, detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks. To solve this problem, a new method of DoS attack detection based on Deep Factorization Machine (DeepFM) is proposed in SDN. Firstly, we select the Growth Rate of Max Matched Packets (GRMMP) in SDN as detection feature. Then, the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks. After training, the model can be used to infer whether SDN is under DoS attacks, and a DeepFM-based detection method for DoS attacks against client host is implemented. Simulation results show that our method can effectively detect DoS attacks in SDN. Compared with the K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN) models, Support Vector Machine (SVM) and Random Forest models, our proposed method outperforms in accuracy, precision and F1 values.

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Keywords

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