Decision Tree with Sensitive Pruning in Network-based Intrusion Detection System

Yee Jian Chew; Shih Yin Ooi; Kok Seng Wong; Ying Han Pang

doi:10.1007/978-981-15-0058-9_1

Decision Tree with Sensitive Pruning in Network-based Intrusion Detection System

Yee Jian Chew, Shih Yin Ooi, Kok Seng Wong, Ying Han Pang

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

1 Citation (Scopus)

Abstract

Machine learning techniques have been extensively adopted in the domain of Network-based Intrusion Detection System (NIDS) especially in the task of network traffics classification. A decision tree model with its kinship terminology is very suitable in this application. The merit of its straightforward and simple “if-else” rules makes the interpretation of network traffics easier. Despite its powerful classification and interpretation capacities, the visibility of its tree rules is introducing a new privacy risk to NIDS where it reveals the network posture of the owner. In this paper, we propose a sensitive pruning-based decision tree to tackle the privacy issues in this domain. The proposed pruning algorithm is modified based on C4.8 decision tree (better known as J48 in Weka package). The proposed model is tested with the 6 percent GureKDDCup NIDS dataset.

Original language	English
Title of host publication	Computational Science and Technology - 6th ICCST 2019
Editors	Rayner Alfred, Yuto Lim, Haviluddin Haviluddin, Chin Kim On
Publisher	Springer Verlag
Pages	1-10
Number of pages	10
ISBN (Print)	9789811500572
DOIs	https://doi.org/10.1007/978-981-15-0058-9_1
Publication status	Published - Jan 1 2020
Event	6th International Conference on Computational Science and Technology, ICCST 2019 - Kota Kinabalu, Malaysia Duration: Aug 29 2019 → Aug 30 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	603
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	6th International Conference on Computational Science and Technology, ICCST 2019
Country	Malaysia
City	Kota Kinabalu
Period	8/29/19 → 8/30/19

Keywords

Decision Tree
GureKDDCup
Network-based Intrusion Detection System (NIDS)
Privacy
Sensitive Pruning
Weka J48

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-15-0058-9_1

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Chew, Y. J., Ooi, S. Y., Wong, K. S., & Pang, Y. H. (2020). Decision Tree with Sensitive Pruning in Network-based Intrusion Detection System. In R. Alfred, Y. Lim, H. Haviluddin, & C. K. On (Eds.), Computational Science and Technology - 6th ICCST 2019 (pp. 1-10). (Lecture Notes in Electrical Engineering; Vol. 603). Springer Verlag. https://doi.org/10.1007/978-981-15-0058-9_1

Decision Tree with Sensitive Pruning in Network-based Intrusion Detection System. / Chew, Yee Jian; Ooi, Shih Yin; Wong, Kok Seng; Pang, Ying Han.

Computational Science and Technology - 6th ICCST 2019. ed. / Rayner Alfred; Yuto Lim; Haviluddin Haviluddin; Chin Kim On. Springer Verlag, 2020. p. 1-10 (Lecture Notes in Electrical Engineering; Vol. 603).

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

Chew, YJ, Ooi, SY, Wong, KS & Pang, YH 2020, Decision Tree with Sensitive Pruning in Network-based Intrusion Detection System. in R Alfred, Y Lim, H Haviluddin & CK On (eds), Computational Science and Technology - 6th ICCST 2019. Lecture Notes in Electrical Engineering, vol. 603, Springer Verlag, pp. 1-10, 6th International Conference on Computational Science and Technology, ICCST 2019, Kota Kinabalu, Malaysia, 8/29/19. https://doi.org/10.1007/978-981-15-0058-9_1

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abstract = "Machine learning techniques have been extensively adopted in the domain of Network-based Intrusion Detection System (NIDS) especially in the task of network traffics classification. A decision tree model with its kinship terminology is very suitable in this application. The merit of its straightforward and simple “if-else” rules makes the interpretation of network traffics easier. Despite its powerful classification and interpretation capacities, the visibility of its tree rules is introducing a new privacy risk to NIDS where it reveals the network posture of the owner. In this paper, we propose a sensitive pruning-based decision tree to tackle the privacy issues in this domain. The proposed pruning algorithm is modified based on C4.8 decision tree (better known as J48 in Weka package). The proposed model is tested with the 6 percent GureKDDCup NIDS dataset.",

keywords = "Decision Tree, GureKDDCup, Network-based Intrusion Detection System (NIDS), Privacy, Sensitive Pruning, Weka J48",

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note = "Funding Information: This research work was supported by a Fundamental Research Grant Schemes (FRGS) under the Ministry of Education and Multimedia University, Malaysia (Project ID: MMUE/160029), and Korea Foundation of Advanced Studies (ISEF).; 6th International Conference on Computational Science and Technology, ICCST 2019 ; Conference date: 29-08-2019 Through 30-08-2019",

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N1 - Funding Information: This research work was supported by a Fundamental Research Grant Schemes (FRGS) under the Ministry of Education and Multimedia University, Malaysia (Project ID: MMUE/160029), and Korea Foundation of Advanced Studies (ISEF).

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Machine learning techniques have been extensively adopted in the domain of Network-based Intrusion Detection System (NIDS) especially in the task of network traffics classification. A decision tree model with its kinship terminology is very suitable in this application. The merit of its straightforward and simple “if-else” rules makes the interpretation of network traffics easier. Despite its powerful classification and interpretation capacities, the visibility of its tree rules is introducing a new privacy risk to NIDS where it reveals the network posture of the owner. In this paper, we propose a sensitive pruning-based decision tree to tackle the privacy issues in this domain. The proposed pruning algorithm is modified based on C4.8 decision tree (better known as J48 in Weka package). The proposed model is tested with the 6 percent GureKDDCup NIDS dataset.

AB - Machine learning techniques have been extensively adopted in the domain of Network-based Intrusion Detection System (NIDS) especially in the task of network traffics classification. A decision tree model with its kinship terminology is very suitable in this application. The merit of its straightforward and simple “if-else” rules makes the interpretation of network traffics easier. Despite its powerful classification and interpretation capacities, the visibility of its tree rules is introducing a new privacy risk to NIDS where it reveals the network posture of the owner. In this paper, we propose a sensitive pruning-based decision tree to tackle the privacy issues in this domain. The proposed pruning algorithm is modified based on C4.8 decision tree (better known as J48 in Weka package). The proposed model is tested with the 6 percent GureKDDCup NIDS dataset.

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Decision Tree with Sensitive Pruning in Network-based Intrusion Detection System

Abstract

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Keywords

ASJC Scopus subject areas

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