Symmetry-Adapted Machine Learning for Information Security
Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their met...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/6/1044 |
| _version_ | 1797564415857393664 |
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| author | Jong Hyuk Park |
| author_facet | Jong Hyuk Park |
| author_sort | Jong Hyuk Park |
| collection | DOAJ |
| description | Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for supporting defense against security threats. However, these existing approaches have failed to deal with security challenges in next-generation ICT systems due to the changing behaviors of security threats and zero-day attacks, including advanced persistent threat (APT), ransomware, and supply chain attacks. The symmetry-adapted machine-learning approach can support an effective way to deal with the dynamic nature of security attacks by the extraction and analysis of data to identify hidden patterns of data. It offers the identification of unknown and new attack patterns by extracting hidden data patterns in next-generation ICT systems. Therefore, we accepted twelve articles for this Special Issue that explore the deployment of symmetry-adapted machine learning for information security in various application areas. These areas include malware classification, intrusion detection systems, image watermarking, color image watermarking, battlefield target aggregation behavior recognition models, Internet Protocol (IP) cameras, Internet of Things (IoT) security, service function chains, indoor positioning systems, and cryptoanalysis. |
| first_indexed | 2024-03-10T18:58:01Z |
| format | Article |
| id | doaj.art-234884c53e86421086c297f86975073c |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T18:58:01Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-234884c53e86421086c297f86975073c2023-11-20T04:37:01ZengMDPI AGSymmetry2073-89942020-06-01126104410.3390/sym12061044Symmetry-Adapted Machine Learning for Information SecurityJong Hyuk Park0Department of Computer Science and Engineering, Seoul National University of Science and Technology (SeoulTech), 232 Gongneung-ro, Nowon-gu, Seoul 01811, KoreaNowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for supporting defense against security threats. However, these existing approaches have failed to deal with security challenges in next-generation ICT systems due to the changing behaviors of security threats and zero-day attacks, including advanced persistent threat (APT), ransomware, and supply chain attacks. The symmetry-adapted machine-learning approach can support an effective way to deal with the dynamic nature of security attacks by the extraction and analysis of data to identify hidden patterns of data. It offers the identification of unknown and new attack patterns by extracting hidden data patterns in next-generation ICT systems. Therefore, we accepted twelve articles for this Special Issue that explore the deployment of symmetry-adapted machine learning for information security in various application areas. These areas include malware classification, intrusion detection systems, image watermarking, color image watermarking, battlefield target aggregation behavior recognition models, Internet Protocol (IP) cameras, Internet of Things (IoT) security, service function chains, indoor positioning systems, and cryptoanalysis.https://www.mdpi.com/2073-8994/12/6/1044symmetryintrusion detection systemmachine learningimage watermarkinginformation securityIoT security |
| spellingShingle | Jong Hyuk Park Symmetry-Adapted Machine Learning for Information Security Symmetry symmetry intrusion detection system machine learning image watermarking information security IoT security |
| title | Symmetry-Adapted Machine Learning for Information Security |
| title_full | Symmetry-Adapted Machine Learning for Information Security |
| title_fullStr | Symmetry-Adapted Machine Learning for Information Security |
| title_full_unstemmed | Symmetry-Adapted Machine Learning for Information Security |
| title_short | Symmetry-Adapted Machine Learning for Information Security |
| title_sort | symmetry adapted machine learning for information security |
| topic | symmetry intrusion detection system machine learning image watermarking information security IoT security |
| url | https://www.mdpi.com/2073-8994/12/6/1044 |
| work_keys_str_mv | AT jonghyukpark symmetryadaptedmachinelearningforinformationsecurity |