Dynamic learning model update of hybrid-classifiers for intrusion detection
- Authors
- Cho, Jaeik; Shon, Taeshik; Choi, Ken; Moon, Jongsub
- Issue Date
- 5월-2013
- Publisher
- SPRINGER
- Keywords
- Intrusion detection; Machine learning; Data set update; Network security; Pattern recognition
- Citation
- JOURNAL OF SUPERCOMPUTING, v.64, no.2, pp.522 - 526
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF SUPERCOMPUTING
- Volume
- 64
- Number
- 2
- Start Page
- 522
- End Page
- 526
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/103379
- DOI
- 10.1007/s11227-011-0698-x
- ISSN
- 0920-8542
- Abstract
- Machine Learning as network attack detection is one of the popular methods researched. Signature based network attack detection is no longer convinced the efficiency in the diversified intrusions (Limmer and Dressler in 17th ACM Conference on Computer and Communication Security, 2010). Moreover, as the various Zero-day attacks, non notified attacks cannot be detected (Wu and Banzhaf in Appl Soft Comput 10(1):1-35, 2010). This paper suggests an effective update method of data set on Machine Learning to detect non notified attacks. In addition, this paper compares and verifies the effects of Machine Learning Detection with updated data set to the former methods.
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Collections - College of Science and Technology > Department of Electronics and Information Engineering > 1. Journal Articles
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