| Title | Analysis of Data Transforming Technology for Malware Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Han, Sung-Hwa |
| Conference Name | 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter) |
| Keywords | data conversion, Deep Learning, detection rate, GBM, Human Behavior, learning (artificial intelligence), machine learning, malicious script, Malware, malware analysis, Metrics, privacy, pubcrawl, resilience, Resiliency, software engineering, Training data |
| Abstract | 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. |
| DOI | 10.1109/SNPDWinter52325.2021.00055 |
| Citation Key | han_analysis_2021 |
Analysis of Data Transforming Technology for Malware Detection