Zobrazeno 1 - 10 of 31 pro vyhledávání: '"Cheolhee Park"'
1
Akademický článek
A study of the relationship of malware detection mechanisms using Artificial Intelligence
Autor: Jihyeon Song, Sunoh Choi, Jungtae Kim, Kyungmin Park, Cheolhee Park, Jonghyun Kim, Ikkyun Kim
Publikováno v: ICT Express, Vol 10, Iss 3, Pp 632-649 (2024)
Implementation of malware detection using Artificial Intelligence (AI) has emerged as a significant research theme to combat evolving various types of malwares. Researchers implement various detection mechanisms using shallow and deep learning models
Externí odkaz: https://doaj.org/article/28733a9d250d44dab2592bbb15bd8b72
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2
Akademický článek
Evaluating Differentially Private Generative Adversarial Networks Over Membership Inference Attack
Autor: Cheolhee Park, Youngsoo Kim, Jong-Geun Park, Dowon Hong, Changho Seo
Publikováno v: IEEE Access, Vol 9, Pp 167412-167425 (2021)
As communication technology advances with 5G, the amount of data accumulated online is explosively increasing. From these data, valuable results are being created through data analysis technologies. Among them, artificial intelligence (AI) has shown
Externí odkaz: https://doaj.org/article/ddd301e14fcc4ee1b06b45d8b32ee535
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3
Akademický článek
An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack
Autor: Cheolhee Park, Dowon Hong, Changho Seo
Publikováno v: IEEE Access, Vol 7, Pp 124988-124999 (2019)
As the amount of data and computational power explosively increase, valuable results are being created using machine learning techniques. In particular, models based on deep neural networks have shown remarkable performance in various domains. On the
Externí odkaz: https://doaj.org/article/ef9b8eebef23449798f034b431bcb3e3
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8
Evaluating differentially private decision tree model over model inversion attack
Autor: Changho Seo, Cheolhee Park, Dowon Hong
Publikováno v: International Journal of Information Security. 21:1-14
Machine learning techniques have been widely used and shown remarkable performance in various fields. Along with the widespread utilization of machine learning, concerns about privacy violations have been raised. Recently, as privacy invasion attacks
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8e84cda9151d48bfe81c791535839136
https://doi.org/10.1007/s10207-021-00564-5
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