Authors: Gang Ke; Ruey-Shun Chen; Yeh-Cheng Chen
Addresses: Department of Computer Engineering, Dongguan Polytechnic, Dongguan, Guangdong, China ' Department of Computer Engineering, Dongguan Polytechnic, Dongguan, Guangdong, China ' Department of Computer Science, University of California, Davis, CA, USA
Abstract: Aiming at the problems of low accuracy and high false alarm rate when traditional machine learning algorithm processes massive and complex intrusion detection data, this paper proposes a network intrusion detection method (SMOTE-DBN-LSSVM) which combines deep belief network (DBN), synthetic minority oversampling technique (SMOTE) and least squares support vector machine (LSSVM). In this algorithm, intrusion detection data is input to the DBN for depth feature extraction, and then a small number of samples are added through SMOTE algorithm. Finally, LSSVM is used for classification. Through the effective evaluation of SMOTE-DBN-LSSVM model by NSL-KDD dataset, the experimental results show that SMOTE-DBN-LSSVM algorithm has the advantages of high accuracy and low false alarm rate compared with other algorithms, and improves the detection rate of small sample attacks.
Keywords: deep belief network; DBN; LSSVM; synthetic minority oversampling technique; SMOTE; intrusion detection; NSL-KDD dataset.
International Journal of Information and Computer Security, 2022 Vol.18 No.3/4, pp.300 - 312
Received: 17 Dec 2019
Accepted: 05 Mar 2020
Published online: 05 Sep 2022 *