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학술지 A Survey of Deep Learning-based Network Anomaly Detection
Cited 37 time in scopus
저자
권동운, 김현주, 김진오, 서상천, 김익균, 김귀남
발행일
201901
출처
Cluster Computing, v.22 no.Supp.1, pp.949-961
ISSN
1386-7857
출판사
Springer
DOI
https://dx.doi.org/10.1007/s10586-017-1117-8
협약과제
17HH1900, 맞춤형 보안서비스 제공을 위한 클라우드 기반 지능형 보안 기술 개발, 김종현
초록
© 2017, Springer Science+Business Media, LLC. A great deal of attention has been given to deep learning over the past several years, and new deep learning techniques are emerging with improved functionality. Many computer and network applications actively utilize such deep learning algorithms and report enhanced performance through them. In this study, we present an overview of deep learning methodologies, including restricted Bolzmann machine-based deep belief network, deep neural network, and recurrent neural network, as well as the machine learning techniques relevant to network anomaly detection. In addition, this article introduces the latest work that employed deep learning techniques with the focus on network anomaly detection through the extensive literature survey. We also discuss our local experiments showing the feasibility of the deep learning approach to network traffic analysis.
키워드
Deep learning, Intrusion detection, Network anomaly detection, Network security, Network traffic analysis
KSP 제안 키워드
Deep belief network(DBN), Deep neural network(DNN), Learning approach, Learning-based, Machine Learning technique(MLT), Network anomaly detection, Network applications, Network traffic analysis, Recurrent Neural Network(RNN), deep learning(DL), enhanced performance