• May 15, 2019 News!Vol.7, No.5- Vol.8, No.4 has been indexed by EI (Inspec).   [Click]
  • Aug 01, 2018 News! [CFP] 2020 the annual meeting of IJMO Editorial Board, ECDMO 2020, will be held in Athens, Greece, February 15-17, 2020.   [Click]
  • Sep 30, 2019 News!Vol 9, No 6 has been published with online version. 12 original aritcles from 6 countries are published in this issue.    [Click]
General Information
    • ISSN: 2010-3697  (Online)
    • Abbreviated Title: Int. J. Model. Optim.
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET), EBSCO, etc.
    • E-mail ijmo@iacsitp.com
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2012 Vol.2(4): 488-492 ISSN: 2010-3697
DOI: 10.7763/IJMO.2012.V2.168

Intrusion Detection System Using New Ensemble Boosting Approach

Snehlata S. Dongre and Kapil K. Wankhade

Abstract—Security is a big issue for all networks in today’s enterprise environment. Hackers and intruders have made many successful attempts to bring down high profile company networks and web services. Intrusion Detection System (IDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. The main reason for using data mining classification methods for Intrusion Detection System is due to the enormous volume of existing and newly appearing network data that require processing. Data mining is the best option for dandling such type of data. This paper presents the new idea of applying data mining classification techniques to intrusion detection systems to maximize the effectiveness in identifying attacks, thereby helping the users to construct more secure information systems. This paper uses ensemble boosting approach with adaptive sliding window for intrusion detection. The ensemble method is advantageous over single classifier.

Index Terms—Adaptive sliding window, data mining, ensemble approach, adaptive sliding window, IDS.

S. S. Dongre is with the Department of Computer Science and Engineering, G. H. Raisoni College of Engineering, Nagpur, Maharashtra, INDIA 440019 (e-mail: dongre.sneha@gmail.com).
K. K. Wankhade is with the Department of Information Technolgy, G. H. Raisoni College of Engineering, Nagpur, Maharashtra, INDIA 440019 (e-mail: kaps.wankhade@gmail.com).


Cite: Snehlata S. Dongre and Kapil K. Wankhade, "Intrusion Detection System Using New Ensemble Boosting Approach," International Journal of Modeling and Optimization vol. 2, no. 4, pp. 488-492, 2012.

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