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Journal Article
Design of Network Threat Detection and Classification based on Machine Learning on Cloud Computing
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- Authors
- Hyunjoo Kim, Jonghyun Kim, Youngsoo Kim, Ikkyun Kim, Kuinam J. Kim
- Issue Date
- 2019-01
- Citation
- Cluster Computing, v.22, no.Supp.1, pp.2341-2350
- ISSN
- 1386-7857
- Publisher
- Springer
- Language
- English
- Type
- Journal Article
- Project Code
- 18HH1400, Cloud based Security Intelligence Technology Development for the Customized Security Service Provisioning, Kim Jonghyun
- Abstract
- To respond to recent network threats that are using increasingly intelligent techniques, the intelligent security technology on cloud computing is required. Especially it supports small and medium enterprises to build IT security solution with low cost and less effort because it is provided as Security as a Service on a cloud environment. In this paper, we particularly propose the network threat detection and classification method based on machine learning, which is a part of the intelligent threat analysis technology. In order to improve the performance of detection and classification of network threat, it was built in a hybrid way such as applying an unsupervised learning approach with unlabeled data, naming clusters with labeled data, and using a supervised learning approach for feature selection.
- KSP Keywords
- Classification method, Cloud Computing, Feature selection(FS), Intelligent security, Intelligent techniques, It security, Learning approach, Low-cost, Network Threat, Security as a Service, Small and Medium-sized Enterprise(SME)