A Survey on Intrusion Detection Systems and Classification Techniques

Authors(2) :-Prof. Javed Akhtar Khan, Nitesh Jain

Today it is very important to provide a high level security to protect highly sensitive and private information. Intrusion Detection System is an essential technology in Network Security. Nowadays researchers have interested on intrusion detection system using Data mining techniques as an artful skill. IDS is a software or hardware device that deals with attacks by collecting information from a variety of system and network sources, then analyzing symptoms of security problems. This paper includes an overview of intrusion detection systems and introduces the reader to some fundamental concepts of IDS methodology. We also discuss the primary intrusion detection techniques. In this paper, we emphasizes data mining algorithms to implement IDS such as Support Vector Machine, Kernelized support vector machine, Extreme Learning Machine and Kernelized Extreme Learning Machine.

Authors and Affiliations

Prof. Javed Akhtar Khan
Department of Computer Science & Engineering, Takshshila Institute of Engineering & Technology, Jabalpur, Madhya Pradesh, India
Nitesh Jain
M. Tech Scholar, Takshshila institute of engineering & technology, Jabalpur, Madhya Pradesh, India

SVM, KELM, Intrusion Detection System, Data Mining and IDS, ELM, Classification Techniques for IDS, KSVM

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2016
Date of Publication : 2016-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 202-208
Manuscript Number : IJSRSET162561
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Prof. Javed Akhtar Khan, Nitesh Jain , " A Survey on Intrusion Detection Systems and Classification Techniques, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.202-208, September-October-2016.
Journal URL : http://ijsrset.com/IJSRSET162561

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