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

doi:10.1109/JAS.2020.1003099

CASIA OpenIR > 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
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2020
卷号	7 期号:3 页码:790-799
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.
关键词	Deep belief network (DBN) flow calculation frequent pattern intrusion detection sliding window support vector machine (SVM)
DOI	10.1109/JAS.2020.1003099
引用统计	被引频次：70[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42989
专题	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.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
JAS-2018-0342.pdf（3055KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载