Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification

Jingping Song; Zhiliang Zhu; Peter Matthew David Scully; Christopher Price

Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification

Jingping Song, Zhiliang Zhu, Peter Matthew David Scully, Christopher Price

Department of Computer Science

Software College of Northeastern University

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

15 Citations (Scopus)

Abstract

In this work, a new method for classification is proposed consisting of a combination of feature selection, normalization, fuzzy C means clustering algorithm and C4.5 decision tree algorithm. The aim of this method is to improve the performance of the classifier by using selected features. The fuzzy C means clustering method is used to partition the training instances into clusters. On each cluster, we build a decision tree using C4.5 algorithm. Experiments on the KDD CUP 99 data set shows that our proposed method in detecting intrusion achieves better performance while reducing the relevant features by more than 80%.

Original language	English
Title of host publication	Cyberspace Safety and Security
Subtitle of host publication	Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013
Editors	Guojun Wang
Publisher	Springer Nature
Pages	299-307
Volume	8300
ISBN (Electronic)	978-3-319-03584-0
ISBN (Print)	978-3-319-03583-3, 3319035835
Publication status	Published - 11 Nov 2013

Publication series

Name	Lecture Notes in Computer Science / Security and Cryptology
Publisher	Springer

Keywords

Intrusion detection
Fuzzy C-Means
Feature selection
C4.5

Permanent link

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Song, J., Zhu, Z., Scully, P. M. D., & Price, C. (2013). Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification. In G. Wang (Ed.), Cyberspace Safety and Security: Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013 (Vol. 8300, pp. 299-307). (Lecture Notes in Computer Science / Security and Cryptology). Springer Nature.

Song, Jingping ; Zhu, Zhiliang ; Scully, Peter Matthew David et al. / Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification. Cyberspace Safety and Security: Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013 . editor / Guojun Wang. Vol. 8300 Springer Nature, 2013. pp. 299-307 (Lecture Notes in Computer Science / Security and Cryptology).

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title = "Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification",

abstract = "In this work, a new method for classification is proposed consisting of a combination of feature selection, normalization, fuzzy C means clustering algorithm and C4.5 decision tree algorithm. The aim of this method is to improve the performance of the classifier by using selected features. The fuzzy C means clustering method is used to partition the training instances into clusters. On each cluster, we build a decision tree using C4.5 algorithm. Experiments on the KDD CUP 99 data set shows that our proposed method in detecting intrusion achieves better performance while reducing the relevant features by more than 80%.",

keywords = "Intrusion detection, Fuzzy C-Means , Feature selection , C4.5",

author = "Jingping Song and Zhiliang Zhu and Scully, {Peter Matthew David} and Christopher Price",

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publisher = "Springer Nature",

pages = "299--307",

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Song, J, Zhu, Z, Scully, PMD & Price, C 2013, Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification. in G Wang (ed.), Cyberspace Safety and Security: Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013 . vol. 8300, Lecture Notes in Computer Science / Security and Cryptology, Springer Nature, pp. 299-307.

Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification. / Song, Jingping; Zhu, Zhiliang; Scully, Peter Matthew David et al.
Cyberspace Safety and Security: Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013 . ed. / Guojun Wang. Vol. 8300 Springer Nature, 2013. p. 299-307 (Lecture Notes in Computer Science / Security and Cryptology).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

TY - GEN

T1 - Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification

AU - Song, Jingping

AU - Zhu, Zhiliang

AU - Scully, Peter Matthew David

AU - Price, Christopher

N1 - .

PY - 2013/11/11

Y1 - 2013/11/11

N2 - In this work, a new method for classification is proposed consisting of a combination of feature selection, normalization, fuzzy C means clustering algorithm and C4.5 decision tree algorithm. The aim of this method is to improve the performance of the classifier by using selected features. The fuzzy C means clustering method is used to partition the training instances into clusters. On each cluster, we build a decision tree using C4.5 algorithm. Experiments on the KDD CUP 99 data set shows that our proposed method in detecting intrusion achieves better performance while reducing the relevant features by more than 80%.

AB - In this work, a new method for classification is proposed consisting of a combination of feature selection, normalization, fuzzy C means clustering algorithm and C4.5 decision tree algorithm. The aim of this method is to improve the performance of the classifier by using selected features. The fuzzy C means clustering method is used to partition the training instances into clusters. On each cluster, we build a decision tree using C4.5 algorithm. Experiments on the KDD CUP 99 data set shows that our proposed method in detecting intrusion achieves better performance while reducing the relevant features by more than 80%.

KW - Intrusion detection

KW - Fuzzy C-Means

KW - Feature selection

KW - C4.5

UR - http://hdl.handle.net/2160/44490

M3 - Conference Proceeding (Non-Journal item)

SN - 978-3-319-03583-3

SN - 3319035835

VL - 8300

T3 - Lecture Notes in Computer Science / Security and Cryptology

SP - 299

EP - 307

BT - Cyberspace Safety and Security

A2 - Wang, Guojun

PB - Springer Nature

ER -

Song J, Zhu Z, Scully PMD, Price C. Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification. In Wang G, editor, Cyberspace Safety and Security: Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013 . Vol. 8300. Springer Nature. 2013. p. 299-307. (Lecture Notes in Computer Science / Security and Cryptology).

Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification

Abstract

Publication series

Keywords

Permanent link

Other files and links

Fingerprint

Self-heating architectures against malware propagation

Cite this

Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification

Abstract

Publication series

Keywords

Permanent link

Other files and links

Fingerprint

Projects

Self-heating architectures against malware propagation

Cite this