.png){kind=icon}
返回
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}
分享.png){kind=icon}
收藏
摘要
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总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.0W
被引数:
83.5W
机构
引用论文
Derivation of quantum propagator for coupled harmonic oscillator with uniform electric field in a single harmonic oscillator environment using white noise functional approach用白噪声泛函方法推导单谐振子环境中具有均匀电场耦合谐振子的量子传播子OAAI
OAAI分享收藏Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Malware Detection of Hangul Word Processor Files Using Spatial Pyramid Average PoolingSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
分享收藏分享收藏 没有更多内容
没有更多内容