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내부정보 유출 시나리오와 Data Analytics 기법을 활용한 내부정보 유출징후 탐지 모형 개발에 관한 연구
박현출,
박진상,
김정덕,
Vol. 30, No. 5, pp. 957-966,
10월.
2020
10.13089/JKIISC.2020.30.5.957
주제어: Internal information leak, scenario, data analytics, Anomaly Detection, risk indicators, Internal information leak, scenario, data analytics, Anomaly Detection, risk indicators
10.13089/JKIISC.2020.30.5.957
주제어: Internal information leak, scenario, data analytics, Anomaly Detection, risk indicators, Internal information leak, scenario, data analytics, Anomaly Detection, risk indicators
AI를 통한 BEC (Business Email Compromise) 공격의 효과적인 대응방안 연구
이도경,
장건수,
이경호,
Vol. 30, No. 5, pp. 835-846,
10월.
2020
10.13089/JKIISC.2020.30.5.835
주제어: Business email compromise, BEC, SCAM, Social Engineering, email attack, Machine Learning, Business email compromise, BEC, SCAM, Social Engineering, email attack, Machine Learning
10.13089/JKIISC.2020.30.5.835
주제어: Business email compromise, BEC, SCAM, Social Engineering, email attack, Machine Learning, Business email compromise, BEC, SCAM, Social Engineering, email attack, Machine Learning
CNN을 이용한 소비 전력 파형 기반 명령어 수준 역어셈블러 구현
배대현,
하재철,
Vol. 30, No. 4, pp. 527-536,
8월.
2020
10.13089/JKIISC.2020.30.4.527
주제어: Side-Channel Attack, power analysis, Deep Learning, Convolutional Neural Network(CNN), Disassembl, Side-Channel Attack, power analysis, Deep Learning, Convolutional Neural Network(CNN), Disassembl
10.13089/JKIISC.2020.30.4.527
주제어: Side-Channel Attack, power analysis, Deep Learning, Convolutional Neural Network(CNN), Disassembl, Side-Channel Attack, power analysis, Deep Learning, Convolutional Neural Network(CNN), Disassembl
