디지털산업정보학회논문지 (Journal of Korea Society of Digital Industry and Information Management)

  • 제11권4호
  • /
  • Pages.33-45
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  • 2015
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  • 1738-6667(pISSN)
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  • 2713-9018(eISSN)
디지털산업정보학회 (Korea Society of Digital Industry and Information Management)
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의사결정트리와 인공 신경망 기법을 이용한 침입탐지 효율성 비교 연구

A Comparative Study on the Performance of Intrusion Detection using Decision Tree and Artificial Neural Network Models

  • 조성래 (경상대학교 대학원 경영정보학과) ;
  • 성행남 (경상대학교 경영대학) ;
  • 안병혁 (경상대학교 경영대학 경영정보학과, 경영경제연구소)
  • 투고 : 2015.11.16
  • 심사 : 2015.11.25
  • 발행 : 2015.12.30
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초록

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.

키워드

참고문헌

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피인용 문헌

  1. An Efficient Hybrid Self-Learning Intrusion Detection System Based on Neural Networks pp.1757-5885, 2019, https://doi.org/10.1142/S1469026819500019
  2. 자가적응모듈과 퍼지인식도가 적용된 하이브리드 침입시도탐지모델 vol.13, pp.3, 2015, https://doi.org/10.17662/ksdim.2017.13.3.019