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박상환(Sanghwan Park),편종권(Jongkweon Pyun),김형준(Hyeongjun Kim),황수환(Suhwan Hwang),이미현(Mihyun Lee),김동기(Donggi Kim),김정찬(Jeongchan Kim) 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
Integration of functions currently being applied to develop and integrate the controller is analyzed by the synergy. And the process of developing integrated control over the results obtained from the designers of future vehicle to suggest approaches to controller design is aimed.
LID-DS 데이터 세트를 사용한 기계학습 알고리즘 비교 연구
박대경 ( Park Daekyeong ),류경준 ( Ryu Kyungjoon ),신동일 ( Shin Dongil ),신동규 ( Shin Dongkyoo ),박정찬 ( Park Jeongchan ),김진국 ( Kim Jingoog ) 한국정보처리학회 2021 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.10 No.3
Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.