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Volume 8 | Issue 1

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Paper Title: Use of Ensemble & Hybrid Classifiers for Intrusion Detection Systems
Authors Name: Preeti Narooka , Randeep Kaur Kahlon , Shaveta Malik , Sonali Dhamele , Dnyaneshwar Bavkar
Unique Id: IJSDR1902049
Published In: Volume 4 Issue 2, February-2019
Abstract: There is an increase in frequency of malicious network activities and network policy violations. To combat the unauthorized use of a network’s resources, intrusion detection systems (IDSs) have emerged. A wide variety of machine learning methods which can be integrated into an IDS have been produced by recent advances in information technology. An overview of intrusion classification algorithms, based on popular methods in the field of machine learning has been presented in this study. Specifically, various ensemble and hybrid techniques were examined, considering both homogeneous and heterogeneous types of ensemble methods. Ensemble methods which are the simplest to implement and generally produce favourable results typically which are based on voting techniques were given special attention. A survey of recent literature shows that hybrid methods, where feature selection or a feature reduction component is combined with a single-stage classifier, have become commonplace. Therefore, the scope of this study has been expanded to encompass hybrid classifiers.
Keywords: Ensemble classifiers, Hybrid classifiers, Intrusion detection
Cite Article: "Use of Ensemble & Hybrid Classifiers for Intrusion Detection Systems ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.4, Issue 2, page no.300 - 305, February-2019, Available :http://www.ijsdr.org/papers/IJSDR1902049.pdf
Downloads: 000201508
Publication Details: Published Paper ID: IJSDR1902049
Registration ID:190129
Published In: Volume 4 Issue 2, February-2019
DOI (Digital Object Identifier):
Page No: 300 - 305
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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