Intrusion detection system model using Fuzzy Kernel C-Means and Laplacian Score feature selection

Z. Rustam, J. Maharani

Research output: Contribution to journalConference articlepeer-review


Nowadays, where the technology dominates every field and activity like transaction, learning activities, private or corporate data storage, and so on are things that we have to look in detail especially in terms of security both in data storage and technology utilization. The trust of the security itself can be represented by the accuracy on model we used but the question is "Which model is good for the security?", that question can be answered by the research or trial of models either by combining the models or construct the new one. Therefore, in this paper we tried an intrusion detection system model by combining Fuzzy Kernel C-Means method as a classifier and Laplacian Score method as a feature selection applied on KDD Cup 99 Data Set. As a result, we will see the comparison of the accuracy of intrusion detection system (IDS) model using Fuzzy Kernel C-means with and without Laplacian Score Feature Selection for the same data and features that resulted 60% and 67.06%, respectively.

Original languageEnglish
Article number012042
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 29 Jan 2020
EventBasic and Applied Sciences Interdisciplinary Conference 2017, BASIC 2017 - , Indonesia
Duration: 18 Aug 201719 Aug 2017


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