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On-Line Intrusion Detection Model Based on Approximate Linear Dependent Condition with Linear Latent Feature Extraction
- Source :
- Cloud Computing and Security ISBN: 9783319685410, ICCCS (2)
- Publication Year :
- 2017
- Publisher :
- Springer International Publishing, 2017.
-
Abstract
- Most of the intrusion detection models (IDM) are constructed with off-line training data. Time-variance characteristic of the practical network system cannot be embodied in the off-line constructed IDM. On-line updating of the off-line IDM with the valued new samples is very necessary. In this paper, a new on-line instruction detection model based on approximate linear dependent (ALD) condition with linear latent feature extraction is proposed to address this problem. Specifically, the valued samples which can represent drift of the practical network are indentified with ALD and prior knowledge. Then, these selected samples are used to update the off-line IDM based on on-line latent feature extraction method and fast machine learning algorithm with sample-based updating strategy. Experiments based on KDD99 data are used to validate the proposed approach.
- Subjects :
- 021110 strategic, defence & security studies
Training set
business.industry
Computer science
Feature extraction
0211 other engineering and technologies
Sample (statistics)
Pattern recognition
02 engineering and technology
Intrusion detection system
Line (geometry)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Linear independence
Artificial intelligence
business
Subjects
Details
- ISBN :
- 978-3-319-68541-0
- ISBNs :
- 9783319685410
- Database :
- OpenAIRE
- Journal :
- Cloud Computing and Security ISBN: 9783319685410, ICCCS (2)
- Accession number :
- edsair.doi...........d31399a6fad4189cc7972d0cce976040