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데이터 예측 클래스 기반 적대적 공격 탐지 및 분류 모델
고은나래,
문종섭,
Vol. 31, No. 6, pp. 1227-1236,
12월.
2021
10.13089/JKIISC.2021.31.6.1227
주제어: Adversarial attack, Evasion Attack, Deep Learning, Adversarial Example Detection
10.13089/JKIISC.2021.31.6.1227
주제어: Adversarial attack, Evasion Attack, Deep Learning, Adversarial Example Detection
선형 판별 분석 및 k-means 알고리즘을 이용한 적대적 공격 유형 분류 방안
최석환,
김형건,
최윤호,
Vol. 31, No. 6, pp. 1215-1225,
12월.
2021
10.13089/JKIISC.2021.31.6.1215
주제어: Deep Learning, Adversarial example, Adversarial attack, clustering
10.13089/JKIISC.2021.31.6.1215
주제어: Deep Learning, Adversarial example, Adversarial attack, clustering
입력 변이에 따른 딥러닝 모델 취약점 연구 및 검증
김재욱,
박래현,
권태경,
Vol. 31, No. 1, pp. 51-59,
2월.
2021
10.13089/JKIISC.2021.31.1.51
주제어: Deep Learning, Mutation, Adversarial machine learning, Deep Learning, Mutation, Adversarial machine learning
10.13089/JKIISC.2021.31.1.51
주제어: Deep Learning, Mutation, Adversarial machine learning, Deep Learning, Mutation, Adversarial machine learning
주파수 분석 기반 RSA 단순 전력 분석
정지혁,
윤지원,
Vol. 31, No. 1, pp. 1-9,
2월.
2021
10.13089/JKIISC.2021.31.1.1
주제어: RSA, Side channel analysis, Automate, K-Means, RSA, Side channel analysis, Automate, K-Means
10.13089/JKIISC.2021.31.1.1
주제어: RSA, Side channel analysis, Automate, K-Means, RSA, Side channel analysis, Automate, K-Means
적대적 공격에 견고한 Perceptual Ad-Blocker 기법
김민재,
김보민,
허준범,
Vol. 30, No. 5, pp. 871-879,
10월.
2020
10.13089/JKIISC.2020.30.5.871
주제어: Adversarial example, Perceptual Ad-Blocker, Defense-GAN, MagNet, Adversarial example, Perceptual Ad-Blocker, Defense-GAN, MagNet
10.13089/JKIISC.2020.30.5.871
주제어: Adversarial example, Perceptual Ad-Blocker, Defense-GAN, MagNet, Adversarial example, Perceptual Ad-Blocker, Defense-GAN, MagNet
감쇠 요소가 적용된 데이터 어그멘테이션을 이용한 대체 모델 학습과 적대적 데이터 생성 방법
민정기,
문종섭,
Vol. 29, No. 6, pp. 1383-1392,
12월.
2019
10.13089/JKIISC.2019.29.6.1383
주제어: Deep Learning, Adversarial Data Generation, data augmentation, Deep Learning, Adversarial Data Generation, data augmentation
10.13089/JKIISC.2019.29.6.1383
주제어: Deep Learning, Adversarial Data Generation, data augmentation, Deep Learning, Adversarial Data Generation, data augmentation
공개 딥러닝 라이브러리에 대한 보안 취약성 검증
정재한,
손태식,
Vol. 29, No. 1, pp. 117-125,
2월.
2019
10.13089/JKIISC.2019.29.1.117
주제어: Adversarial attack, MNIST, Deep Learning, Security, Autoencoder, Convolution Neural Network, Adversarial attack, MNIST, Deep Learning, Security, Autoencoder, Convolution Neural Network
10.13089/JKIISC.2019.29.1.117
주제어: Adversarial attack, MNIST, Deep Learning, Security, Autoencoder, Convolution Neural Network, Adversarial attack, MNIST, Deep Learning, Security, Autoencoder, Convolution Neural Network
RSA 문제와 동등한 안전성을 갖는 온라인/오프라인 서명 기법
최경용,
박종환,
Vol. 28, No. 2, pp. 327-338,
4월.
2018
10.13089/JKIISC.2018.28.2.327
주제어: On-line/off-line signature, RSA problem, tight security reduction, On-line/off-line signature, RSA problem, tight security reduction
10.13089/JKIISC.2018.28.2.327
주제어: On-line/off-line signature, RSA problem, tight security reduction, On-line/off-line signature, RSA problem, tight security reduction
