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

Adran Cyfrifiadureg

Software College of Northeastern University

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

14 Dyfyniadau (Scopus)

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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%.

Iaith wreiddiol	Saesneg
Teitl	Cyberspace Safety and Security
Is-deitl	Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013
Golygyddion	Guojun Wang
Cyhoeddwr	Springer Nature
Tudalennau	299-307
Cyfrol	8300
ISBN (Electronig)	978-3-319-03584-0
ISBN (Argraffiad)	978-3-319-03583-3, 3319035835
Statws	Cyhoeddwyd - 11 Tach 2013

Cyfres gyhoeddiadau

Enw	Lecture Notes in Computer Science / Security and Cryptology
Cyhoeddwr	Springer

Cysylltiad parhaol

Dolen barhaus

Ffeiliau eraill a chysylltiadau

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

1 Wedi Gorffen

Self-heating architectures against malware propagation
Scully, P. M. D. (Prif Ymchwilydd)
01 Hyd 2011 → 30 Medi 2014
Prosiect: Ymchwil a ariannwyd yn allanol

Dyfynnu hyn

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. Yn G. Wang (Gol.), Cyberspace Safety and Security: Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013 (Cyfrol 8300, tt. 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 . Gol. / Guojun Wang. Cyfrol 8300 Springer Nature, 2013. tt. 299-307 (Lecture Notes in Computer Science / Security and Cryptology).

@inproceedings{6e81f86db4334b9a8ba6327f96da8e01,

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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year = "2013",

month = nov,

day = "11",

language = "English",

isbn = "978-3-319-03583-3",

volume = "8300",

series = "Lecture Notes in Computer Science / Security and Cryptology",

publisher = "Springer Nature",

pages = "299--307",

editor = "Guojun Wang",

booktitle = "Cyberspace Safety and Security",

address = "Switzerland",

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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. yn G Wang (gol.), Cyberspace Safety and Security: Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013 . cyfrol. 8300, Lecture Notes in Computer Science / Security and Cryptology, Springer Nature, tt. 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 . gol. / Guojun Wang. Cyfrol 8300 Springer Nature, 2013. t. 299-307 (Lecture Notes in Computer Science / Security and Cryptology).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

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

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BT - Cyberspace Safety and Security

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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. Yn Wang G, golygydd, Cyberspace Safety and Security: Proceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013 . Cyfrol 8300. Springer Nature. 2013. t. 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

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