Biblio

Filters: Author is Han, Sung-Hwa  [Clear All Filters]
2022-02-07
Han, Sung-Hwa.  2021.  Analysis of Data Transforming Technology for Malware Detection. 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter). :224–229.
As AI technology advances and its use increases, efforts to incorporate machine learning for malware detection are increasing. However, for malware learning, a standardized data set is required. Because malware is unstructured data, it cannot be directly learned. In order to solve this problem, many studies have attempted to convert unstructured data into structured data. In this study, the features and limitations of each were analyzed by investigating and analyzing the method of converting unstructured data proposed in each study into structured data. As a result, most of the data conversion techniques suggest conversion mechanisms, but the scope of each technique has not been determined. The resulting data set is not suitable for use as training data because it has infinite properties.