디지털융복합연구 (Journal of Digital Convergence)

  • 제15권6호
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  • Pages.391-398
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  • 2017
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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퍼지와 인공 신경망을 이용한 침입탐지시스템의 탐지 성능 비교 연구

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

  • 양은목 (숭실대학교 소프트웨어학부) ;
  • 이학재 (전남대학교 전자컴퓨터공학과) ;
  • 서창호 (공주대 응용수학과)
  • 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)
  • 투고 : 2017.04.14
  • 심사 : 2017.06.20
  • 발행 : 2017.06.28
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 "퍼지 컨트롤 언어를 이용한 공격 특징 선택기반 네트워크 침입탐지 시스템"[1]과 "RNN을 이용한 공격 분류를 위한 지능형 침입탐지 시스템 모델"[2]의 성능을 비교 하였다. 이 논문에서는 KDD CUP 99 데이터 셋[3]을 이용하여 두 기법의 침입 탐지 성능을 비교하였다. KDD CUP 99 데이터 셋에는 훈련을 위한 데이터 셋과 훈련을 통해 기존의 침입을 탐지 할 수 있는 테스트 데이터 셋이 있다. 또한 훈련 데이터 및 테스트 데이터에 존재 하지 않는 침입의 유형을 탐지할 수 있는가를 테스트 할 수 있는 데이터도 존재한다. 훈련 및 테스트 데이터에서 좋은 침입탐지 성능을 보이는 두 개의 논문을 비교하였다. 비교한 결과 존재하는 침입을 탐지 하는 성능은 우수하지만 기존에 존재하지 않는 침입을 탐지 하는 성능은 부족한 부분이 있다. 공격 유형 중 DoS, Probe, R2L는 퍼지를 이용하는 것이 탐지율이 높았고, U2L은 RNN을 이용하는 것이 탐지율이 높았다.

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.

키워드

참고문헌

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