Automated network feature weighting-based intrusion detection systems

Research output: A Conference proceeding or a Chapter in BookConference contribution

3 Citations (Scopus)

Abstract

A common problem for network intrusion detection systems is that there are many available features describing network traffic and feature values are highly irregular with burst nature. Some values such as octets transferred range several orders of magnitudes, from several bytes to million bytes. The role of network features depends on which pattern to be detected: normal or intrusive one. Intrusion detection rates would be better if we know which network features are more important for a particular pattern. We therefore propose an automated feature weighting method for network intrusion detection based on a fuzzy subspace approach. Experimental results show that the proposed weighting method can improve the detection rates.

Original languageEnglish
Title of host publication2008 IEEE International Conference on System of Systems Engineering, SoSE 2008
Place of PublicationDanvers, USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781424421732
ISBN (Print)9781424421725
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on System of Systems Engineering, SoSE 2008 - Monterey, CA, United States
Duration: 2 Jun 20084 Jun 2008

Conference

Conference2008 IEEE International Conference on System of Systems Engineering, SoSE 2008
CountryUnited States
CityMonterey, CA
Period2/06/084/06/08

Fingerprint

Intrusion detection

Cite this

Tran, D., Wanli, M., & Sharma, D. (2008). Automated network feature weighting-based intrusion detection systems. In 2008 IEEE International Conference on System of Systems Engineering, SoSE 2008 (pp. 1-6). Danvers, USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SYSOSE.2008.4724144
Tran, Dat ; Wanli, Ma ; Sharma, Dharmendra. / Automated network feature weighting-based intrusion detection systems. 2008 IEEE International Conference on System of Systems Engineering, SoSE 2008. Danvers, USA : IEEE, Institute of Electrical and Electronics Engineers, 2008. pp. 1-6
@inproceedings{ce57877708ae46dd9c588872f1781637,
title = "Automated network feature weighting-based intrusion detection systems",
abstract = "A common problem for network intrusion detection systems is that there are many available features describing network traffic and feature values are highly irregular with burst nature. Some values such as octets transferred range several orders of magnitudes, from several bytes to million bytes. The role of network features depends on which pattern to be detected: normal or intrusive one. Intrusion detection rates would be better if we know which network features are more important for a particular pattern. We therefore propose an automated feature weighting method for network intrusion detection based on a fuzzy subspace approach. Experimental results show that the proposed weighting method can improve the detection rates.",
keywords = "Automated feature weighting, Fuzzy c-means, Fuzzy entropy, Network intrusion detection, Subspace vector quantization",
author = "Dat Tran and Ma Wanli and Dharmendra Sharma",
year = "2008",
doi = "10.1109/SYSOSE.2008.4724144",
language = "English",
isbn = "9781424421725",
pages = "1--6",
booktitle = "2008 IEEE International Conference on System of Systems Engineering, SoSE 2008",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
address = "United States",

}

Tran, D, Wanli, M & Sharma, D 2008, Automated network feature weighting-based intrusion detection systems. in 2008 IEEE International Conference on System of Systems Engineering, SoSE 2008. IEEE, Institute of Electrical and Electronics Engineers, Danvers, USA, pp. 1-6, 2008 IEEE International Conference on System of Systems Engineering, SoSE 2008, Monterey, CA, United States, 2/06/08. https://doi.org/10.1109/SYSOSE.2008.4724144

Automated network feature weighting-based intrusion detection systems. / Tran, Dat; Wanli, Ma; Sharma, Dharmendra.

2008 IEEE International Conference on System of Systems Engineering, SoSE 2008. Danvers, USA : IEEE, Institute of Electrical and Electronics Engineers, 2008. p. 1-6.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Automated network feature weighting-based intrusion detection systems

AU - Tran, Dat

AU - Wanli, Ma

AU - Sharma, Dharmendra

PY - 2008

Y1 - 2008

N2 - A common problem for network intrusion detection systems is that there are many available features describing network traffic and feature values are highly irregular with burst nature. Some values such as octets transferred range several orders of magnitudes, from several bytes to million bytes. The role of network features depends on which pattern to be detected: normal or intrusive one. Intrusion detection rates would be better if we know which network features are more important for a particular pattern. We therefore propose an automated feature weighting method for network intrusion detection based on a fuzzy subspace approach. Experimental results show that the proposed weighting method can improve the detection rates.

AB - A common problem for network intrusion detection systems is that there are many available features describing network traffic and feature values are highly irregular with burst nature. Some values such as octets transferred range several orders of magnitudes, from several bytes to million bytes. The role of network features depends on which pattern to be detected: normal or intrusive one. Intrusion detection rates would be better if we know which network features are more important for a particular pattern. We therefore propose an automated feature weighting method for network intrusion detection based on a fuzzy subspace approach. Experimental results show that the proposed weighting method can improve the detection rates.

KW - Automated feature weighting

KW - Fuzzy c-means

KW - Fuzzy entropy

KW - Network intrusion detection

KW - Subspace vector quantization

UR - http://www.scopus.com/inward/record.url?scp=61449094814&partnerID=8YFLogxK

U2 - 10.1109/SYSOSE.2008.4724144

DO - 10.1109/SYSOSE.2008.4724144

M3 - Conference contribution

SN - 9781424421725

SP - 1

EP - 6

BT - 2008 IEEE International Conference on System of Systems Engineering, SoSE 2008

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Danvers, USA

ER -

Tran D, Wanli M, Sharma D. Automated network feature weighting-based intrusion detection systems. In 2008 IEEE International Conference on System of Systems Engineering, SoSE 2008. Danvers, USA: IEEE, Institute of Electrical and Electronics Engineers. 2008. p. 1-6 https://doi.org/10.1109/SYSOSE.2008.4724144