Open AccessComparison between fuzzy robust kernel c-means (FRKCM) and fuzzy entropy kernel c-means (FEKCM) classifier for intrusion detection system (IDS)
Author(s) -
Nedya Shandri,
Zuherman Rustam,
Devvi Sarwinda
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/546/5/052071
Subject(s) - intrusion detection system , computer science , fuzzy logic , artificial intelligence , data mining , kernel (algebra) , the internet , entropy (arrow of time) , pattern recognition (psychology) , classifier (uml) , machine learning , mathematics , operating system , physics , combinatorics , quantum mechanics
Technology is growing very fast. We can now access everything using internet anywhere and anytime. That is why it is important to have internet security since we are always open to an online fraud, property damage and theft. IDS (Intrusion Detection System) can be used to detect any system or network attack. In this empirical study, we use dataset from KDD Cup 1999, which consist of five classes: normal, probe, dos, u2r and r2l. There is some classifier method for IDS, but in this study, we will use Fuzzy Robust Kernel C-Means (FRKCM) with Polynomial kernel and Fuzzy Entropy Kernel C-Means (FEKCM) with RBF kernel to find a better result that increase accuracy of the network attacks. There will be an accuracy comparison between FRKCM method and FEKCM method. The accuracy result from this study is 99% with time execution faster.