Comparison between Support Vector Machine and Fuzzy C-Means as Classifier for Intrusion Detection System

Zuherman Rustam, Durrabida Zahras

Research output: Contribution to journalConference articlepeer-review

23 Citations (Scopus)

Abstract

In this globalization era, cybercrime has been entering every aspect through internet network. The development of Intrusion Detection System (IDS) is being studied deeply to solve the problem. There are several classifier algorithms for Intrusion Detection System such as Support Vector Machine (SVM) and Fuzzy C-Means (FCM). In this study, we will compare proposed model using both Support Vector Machine and Fuzzy C-Means to find a better result that increase accuracy of the network attacks. KDD Cup 1999 will be used to evaluate which algorithms work best. The results are very encouraging and show that SVM and FCM can be a useful tool for intrusion detection system. We found that SVM achieved 94.43% average accuracy rate while FCM achieved 95.09% average accuracy rate.

Original languageEnglish
Article number012227
JournalJournal of Physics: Conference Series
Volume1028
Issue number1
DOIs
Publication statusPublished - 14 Jun 2018
Event2nd International Conference on Statistics, Mathematics, Teaching, and Research 2017, ICSMTR 2017 - Makassar, Indonesia
Duration: 9 Oct 201710 Oct 2017

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