상세정보
학술지
A Survey of Deep Learning-based Network Anomaly Detection
- 발행일
- 201901
- 출처
- Cluster Computing, v.22 no.Supp.1, pp.949-961
- ISSN
- 1386-7857
- 출판사
- Springer
- 협약과제
- 17HH1900, 맞춤형 보안서비스 제공을 위한 클라우드 기반 지능형 보안 기술 개발, 김종현
- 초록
- 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.
- 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