TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Study on Machine Learning Techniques for Malware Classification and Detection |
| 영문제목(English Title) |
Study on Machine Learning Techniques for Malware Classification and Detection |
| 저자(Author) |
Jaewoong Moon
Subin Kim
Jaeseung Song
Kyungshin Kim
|
| 원문수록처(Citation) |
VOL 15 NO. 12 PP. 4308 ~ 4325 (2021. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The importance and necessity of artificial intelligence, particularly machine learning, has recently been emphasized. In fact, artificial intelligence, such as intelligent surveillance cameras and other security systems, is used to solve various problems or provide convenience, providing solutions to problems that humans traditionally had to manually deal with one at a time. Among them, information security is one of the domains where the use of artificial intelligence is especially needed because the frequency of occurrence and processing capacity of dangerous codes exceeds the capabilities of humans. Therefore, this study intends to examine the definition of artificial intelligence and machine learning, its execution method, process, learning algorithm, and cases of utilization in various domains, particularly the cases and contents of artificial intelligence technology used in the field of information security. Based on this, this study proposes a method to apply machine learning technology to the method of classifying and detecting malware that has rapidly increased in recent years. The proposed methodology converts software programs containing malicious codes into images and creates training data suitable for machine learning by preparing data and augmenting the dataset. The model trained using the images created in this manner is expected to be effective in classifying and detecting malware. |
| 키워드(Keyword) |
Machine Learning
Artificial Intelligence
Data Augmentation
Malware detection
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| 파일첨부 |
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