Automated network feature weighting-based intrusion detection systems

Dat Tran; Ma Wanli; Dharmendra Sharma

doi:10.1109/SYSOSE.2008.4724144

Automated network feature weighting-based intrusion detection systems

Dat Tran, Ma Wanli, Dharmendra Sharma

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

3 Citations (Scopus)

53 Downloads (Pure)

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 language	English
Title of host publication	2008 IEEE International Conference on System of Systems Engineering, SoSE 2008
Place of Publication	Danvers, USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781424421732
ISBN (Print)	9781424421725
DOIs	https://doi.org/10.1109/SYSOSE.2008.4724144
Publication status	Published - 2008
Event	2008 IEEE International Conference on System of Systems Engineering, SoSE 2008 - Monterey, CA, United States Duration: 2 Jun 2008 → 4 Jun 2008

Conference

Conference	2008 IEEE International Conference on System of Systems Engineering, SoSE 2008
Country/Territory	United States
City	Monterey, CA
Period	2/06/08 → 4/06/08

Access to Document

10.1109/SYSOSE.2008.4724144

Conference PaperFinal published version, 1.13 MB

Cite this

@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",

note = "2008 IEEE International Conference on System of Systems Engineering, SoSE 2008 ; Conference date: 02-06-2008 Through 04-06-2008",

}

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 Book › Conference contribution › peer-review

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

AN - SCOPUS:61449094814

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

T2 - 2008 IEEE International Conference on System of Systems Engineering, SoSE 2008

Y2 - 2 June 2008 through 4 June 2008

ER -

Automated network feature weighting-based intrusion detection systems

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this