A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine

doi:10.1109/JAS.2020.1003099

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine
	Hao Zhang; Yongdan Li; Zhihan Lv; Arun Kumar Sangaiah; Tao Huang
刊名	IEEE/CAA Journal of Automatica Sinica
	2020
卷号	7 期号:3 页码:790-799
关键词	Deep belief network (DBN) flow calculation frequent pattern intrusion detection sliding window support vector machine (SVM)
ISSN号	2329-9266
DOI	10.1109/JAS.2020.1003099
英文摘要	In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine (DBN-SVM). Sliding window (SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented. Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method’s real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42989]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Hao Zhang,Yongdan Li,Zhihan Lv,et al. A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(3):790-799.
APA	Hao Zhang,Yongdan Li,Zhihan Lv,Arun Kumar Sangaiah,&Tao Huang.(2020).A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine.IEEE/CAA Journal of Automatica Sinica,7(3),790-799.
MLA	Hao Zhang,et al."A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine".IEEE/CAA Journal of Automatica Sinica 7.3(2020):790-799.