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http://dx.doi.org/10.14400/JDC.2017.15.6.391

Comparison of Detection Performance of Intrusion Detection System Using Fuzzy and Artificial Neural Network  

Yang, Eun-Mok (School of Software, Soongsil University)
Lee, Hak-Jae (Dept. of Electronics and Computer Engineering, Chonnam National University)
Seo, Chang-Ho (Dept. of Applied Mathematics, Kongju National University)
Publication Information
Journal of Digital Convergence / v.15, no.6, 2017 , pp. 391-398 More about this Journal
Abstract
In this paper, we compared the performance of "Network Intrusion Detection System based on attack feature selection using fuzzy control language"[1] and "Intelligent Intrusion Detection System Model for attack classification using RNN"[2]. In this paper, we compare the intrusion detection performance of two techniques using KDD CUP 99 dataset. The KDD 99 dataset contains data sets for training and test data sets that can detect existing intrusions through training. There are also data that can test whether training data and the types of intrusions that are not present in the test data can be detected. We compared two papers showing good intrusion detection performance in training and test data. In the comparative paper, there is a lack of performance to detect intrusions that exist but have no existing intrusion detection capability. Among the attack types, DoS, Probe, and R2L have high detection rate using fuzzy and U2L has a high detection rate using RNN.
Keywords
Intrusion Detection; Fuzzy; Neural Network; RNN; KDD CUP 1999 dataset;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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