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싸이킷런과 사이버위협 데이터셋을 이용한 사이버 공격 그룹의 분류
김경신,이호준,김성희,김병익,나원식,김동욱,이정환 중소기업융합학회 2018 융합정보논문지 Vol.8 No.6
최근 IT보안의 화두가 되고 있는 가장 위협적인 공격은 APT공격이다. APT공격에 대한 대응은 인공지능기법을 활용한 대응이외에는 방법이 없다는 것이 현재까지의 결론이다. 여기서는 머신러닝 기법을 활용한 사이버위협 데이터를 분석하는 방법, 그 중에서도 빅데이터 머신러닝 프레임웍인 Scikit Learn를 활용하여 사이버공격 사례를 수집한 데이터셋을 이용하여 사이버공격을 분석하는 머신러닝 알고리즘을 구현하였다. 이 결과 70%에 육박하는 공격 분류 정확도를 보였다. 이 결과는 향후 보안관제 시스템의 알고리즘으로 발전가능하다. The most threatening attack that has become a hot topic of recent IT security is APT Attack.. So far, there is no way to respond to APT attacks except by using artificial intelligence techniques. Here, we have implemented a machine learning algorithm for analyzing cyber threat data using machine learning method, using a data set that collects cyber attack cases using Scikit Learn, a big data machine learning framework . The result showed an attack classification accuracy close to 70%. This result can be developed into the algorithm of the security control system in the future.
김경신 전남대학교 생활과학연구소 2012 生活科學硏究 Vol.20 No.-
The purpose of this study was to find the alternative perspectives through the analysis about practical researches of family studies and to suggest developmental orientations. For this purpose, the paper and conference issues of KFSA and KAFR were investigated and the outcomes or general contents of practical researches focusing on major areas were assessed. The results of this study are as follows: The practical researches have been increased from 1960 and conference issues have been focused on family policy, healthy family and family problems. The alternatives about major areas of practical family researches are exploring of theoretical basis, investigating of identity of healthy family studies, renewal of multi-cultural concepts around family, developing of integrated family policy and especially realizing of family community through practical family studies.