NDLI: A network intrusion detection method based on combined model

Content Provider	IEEE Xplore Digital Library
Author	Cao Li-ying Zhang Xiao-xian Liu He Chen Gui-fen
Copyright Year	2011
Description	Author affiliation: College of Information and Technology Science, Jilin Agricultural University, Changchun, Jilin, China 130118 (Cao Li-ying; Liu He; Chen Gui-fen) \|\| Software Institute, Changchun Institute of Technology, changchun 130012, China (Zhang Xiao-xian)
Abstract	In order to make the detecting rate faster and improve the accuracy of network intrusion detection, this paper ameliorated a network intrusion detection method which was based on combining support vector machines and LVQ (Learning vector quantization) neural network algorithm The method combines the popularizing capability of SVM and the learning capability of LVQ neural network. It overcame the shortcomings of traditional neural network algorithm, such as the slower learning speed and the larger possibility of falling into local minimum. Examples proved that this combined model had faster speed and higher rate of accuracy. What is more, it better resolved a series of detecting problems, such as nonlinearity, small-sample, high-dimension and local minimum.
Starting Page	254
Ending Page	257
File Size	253247
Page Count	4
File Format	PDF
ISBN	9781612847191
e-ISBN	9781612847221
DOI	10.1109/MEC.2011.6025449
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Intrusion Detection Neurons Intrusion detection Support vector machine classification Learning Vector Quantization Neural Network Combined Model Support Vector Machine Kernel Biological neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Network intrusion detection method based on Agent and SVM

Network intrusion classification based on extreme learning machine

Neural network based Support Vector Machine in financial default forecast

An Intrusion Detection Method Based on Multiple Kernel Support Vector Machine

A novel intrusion detection method based on support vector machines

Intrusion Detection Using SVM

Network Intrusion Classification Based on Probabilistic Neural Network

Intrusion Detection System Technique Based on BP-SVM

On the Efficiency of Support Vector Classifiers for intrusion detection

A network intrusion detection method based on combined model

Similar Documents

Network intrusion detection method based on Agent and SVM

Network intrusion classification based on extreme learning machine

Neural network based Support Vector Machine in financial default forecast

An Intrusion Detection Method Based on Multiple Kernel Support Vector Machine

A novel intrusion detection method based on support vector machines

Intrusion Detection Using SVM

Network Intrusion Classification Based on Probabilistic Neural Network

Intrusion Detection System Technique Based on BP-SVM

On the Efficiency of Support Vector Classifiers for intrusion detection

A network intrusion detection method based on combined model