Citation

Chew, Yee Jian and Ooi, Shih Yin and Wong, Kok Seng and Pang, Ying Han and Lee, Nicholas Ming Ze (2022) Adoption of IP Truncation in a Privacy-Based Decision Tree Pruning Design: A Case Study in Network Intrusion Detection System. Electronics (Switzerland), 11 (5). p. 805. ISSN 2079-9292

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Abstract

A decision tree is a transparent model where the rules are visible and can represent the logic of classification. However, this structure might allow attackers to infer confidential information if the rules carry some sensitive information. Thus, a tree pruning methodology based on an IP truncation anonymisation scheme is proposed in this paper to prune the real IP addresses. However, the possible drawback of carelessly designed tree pruning might degrade the performance of the original tree as some information is intentionally opted out for the tree’s consideration. In this work, the 6-percent-GureKDDCup’99, full-version-GureKDDCup’99, UNSW-NB15, and CIDDS-001 datasets are used to evaluate the performance of the proposed pruning method. The results are also compared to the original unpruned tree model to observe its tolerance and trade-off. The tree model adopted in this work is the C4.5 tree. The findings from our empirical results are very encouraging and spell two main advantages: the sensitive IP addresses can be “pruned” (hidden) throughout the classification process to prevent any potential user profiling, and the number of nodes in the tree is tremendously reduced to make the rule interpretation possible while maintaining the classification accuracy.

Item Type:	Article
Uncontrolled Keywords:	Privacy-preserving, IP address truncation
Subjects:	S Agriculture > SB Plant culture
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Apr 2022 01:46
Last Modified:	06 Apr 2022 01:46
URII:	http://shdl.mmu.edu.my/id/eprint/10036

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