.png){kind=icon}
Return
File-level malware detection using byte streams
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-023-36088-2.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As more documents appear on the Internet, it becomes important to detect malware within the documents. Malware of non-executables might be more dangerous because people usually open them without worrying about inherent danger. Recently, deep learning models are used to analyze byte streams of the non-executables for malware detection. Although they have shown successful results, they are commonly designed for stream-level detection, but not for file-level detection. In this paper, we propose a new method that aggregates the stream-level results to get file-level results for malware detection. We demonstrate its effectiveness by experimental results with our annotated dataset, and show that it gives performance gain of 3.37-5.89% of F1 scores.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
28.0W
Citations:
83.5W
Organization
Cited Papers
ShareSaveShareSaveMalware Detection of Hangul Word Processor Files Using Spatial Pyramid Average Pooling.png)
SENSORSOAAI
SENSORSIF3.5
OAAIShareSave
ShareSaveShareSave no more
no more