RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      검색결과 좁혀 보기

      선택해제

      오늘 본 자료

      • 오늘 본 자료가 없습니다.
      더보기
      • 무료
      • 기관 내 무료
      • 유료
      • KCI등재

        Modification of a Density-Based Spatial Clustering Algorithm for Applications with Noise for Data Reduction in Intrusion Detection Systems

        Wiharto,Aditya K. Wicaksana,Denis E. Cahyani 한국지능시스템학회 2021 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.21 No.2

        Monitoring activity in computer networks is required to detect anomalous activities. This monitoring model is known as an intrusion detection system (IDS). Most IDS model developments are based on machine learning. The development of this model requires activity data in the network, either normal or anomalous, in sufficient amounts. The amount of available data also has an impact on the slow learning process in the IDS system, with the resulting performance sometimes not being proportional to the amount of data. This study proposes an IDS model that combines DBSCAN modification with the CART algorithm. DBSCAN modification is performed to reduce data by adding a MinNeighborhood parameter, which is used to determine the distance of the density to the cluster center point, which will then be marked for deletion. The test results, using the Kaggle and KDDCup99 datasets, show that the proposed system model is able to maintain a classification accuracy above 90% for 80% data reduction. This performance was also followed by a decrease in computation time, for the Kaggle dataset from 91.8 ms to 31.1 ms, while for the KDDCup99 dataset from 5.535 seconds to 1.120 seconds.

      연관 검색어 추천

      이 검색어로 많이 본 자료

      활용도 높은 자료

      해외이동버튼