Intrusion detection using a cascade of boosted classifiers (CBC)

Mubasher Baig; El Sayed M. El-Alfy; Mian M. Awais

doi:10.1109/IJCNN.2014.6889931

Intrusion detection using a cascade of boosted classifiers (CBC)

Mubasher Baig^*
, El Sayed M. El-Alfy
, Mian M. Awais

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

A boosting-based cascade for automatic decomposition of multiclass learning problems into several binary classification problems is presented. The proposed cascade structure uses a boosted classifier at each level and use a filtering process to reduce the problem size at each level. The method has been used for detecting malicious traffic patterns using a benchmark intrusion detection dataset. A comparison of the approach with four boosting-based multiclass learning algorithms is also provided on this dataset.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1386-1392
Number of pages	7
ISBN (Electronic)	9781479914845
DOIs	https://doi.org/10.1109/IJCNN.2014.6889931
State	Published - 3 Sep 2014

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

Software
Artificial Intelligence

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10.1109/IJCNN.2014.6889931

Cite this

Baig, M., El-Alfy, E. S. M., & Awais, M. M. (2014). Intrusion detection using a cascade of boosted classifiers (CBC). In Proceedings of the International Joint Conference on Neural Networks (pp. 1386-1392). Article 6889931 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889931

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title = "Intrusion detection using a cascade of boosted classifiers (CBC)",

abstract = "A boosting-based cascade for automatic decomposition of multiclass learning problems into several binary classification problems is presented. The proposed cascade structure uses a boosted classifier at each level and use a filtering process to reduce the problem size at each level. The method has been used for detecting malicious traffic patterns using a benchmark intrusion detection dataset. A comparison of the approach with four boosting-based multiclass learning algorithms is also provided on this dataset.",

author = "Mubasher Baig and El-Alfy, \{El Sayed M.\} and Awais, \{Mian M.\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",

year = "2014",

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doi = "10.1109/IJCNN.2014.6889931",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

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Baig, M, El-Alfy, ESM & Awais, MM 2014, Intrusion detection using a cascade of boosted classifiers (CBC). in Proceedings of the International Joint Conference on Neural Networks., 6889931, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., pp. 1386-1392. https://doi.org/10.1109/IJCNN.2014.6889931

Intrusion detection using a cascade of boosted classifiers (CBC). / Baig, Mubasher; El-Alfy, El Sayed M.; Awais, Mian M.
Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1386-1392 6889931 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Intrusion detection using a cascade of boosted classifiers (CBC)

Abstract

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