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

퍼지와 인공 신경망을 이용한 침입탐지시스템의 탐지 성능 비교 연구

  • 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)
  • 양은목 (숭실대학교 소프트웨어학부) ;
  • 이학재 (전남대학교 전자컴퓨터공학과) ;
  • 서창호 (공주대 응용수학과)
  • Received : 2017.04.14
  • Accepted : 2017.06.20
  • Published : 2017.06.28


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.


Supported by : National Research Foundation of Korea (NRF)


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