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2016 | OriginalPaper | Chapter

Performance Analysis of Multiple Classifier System in DoS Attack Detection

Authors : Bayu Adhi Tama, Kyung Hyune Rhee

Published in: Information Security Applications

Publisher: Springer International Publishing

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Abstract

DoS attacks become a serious attack so as resource protection against this kind of attack is a compulsory task. The major challenge on designing detection scheme using machine learning technique is how to maximize detection rate with lower false alarm. In this paper, we employ and analyze the performance of multiple classifier system (MCS) to detect DoS attack. Several renowned base classifiers such as C4.5, SVM, and k-NN are combined using combination voting scheme and we compare the results with existing ensemble learning algorithms such as Bagging, Adaboost, and Rotation Forest. Based on the experiment using NSL-KDD dataset, MCS scheme has promising performance comparing to existing ensemble learner and single classifier.

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Metadata
Title
Performance Analysis of Multiple Classifier System in DoS Attack Detection
Authors
Bayu Adhi Tama
Kyung Hyune Rhee
Copyright Year
2016
Publisher
Springer International Publishing
Book
Information Security Applications

Print ISBN: 978-3-319-31874-5

Electronic ISBN: 978-3-319-31875-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-31875-2_28

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