可视化
计算机科学
编码(集合论)
特征(语言学)
频道(广播)
数据挖掘
领域(数学)
图像(数学)
过程(计算)
恶意软件
人工智能
模式识别(心理学)
计算机安全
数学
计算机网络
操作系统
语言学
哲学
集合(抽象数据类型)
程序设计语言
纯数学
作者
Xuyan Qi,Wei Liu,Rui Lou,Qinghao Li,Liehui Jiang,Yonghe Tang
出处
期刊:Electronics
[MDPI AG]
日期:2023-05-17
卷期号:12 (10): 2272-2272
标识
DOI:10.3390/electronics12102272
摘要
Malware detection has always been a hot topic in the cyber security field. With continuous research over the years, many research methods and detection tools based on code visualization have been proposed and achieved good results. However, in the process of code visualization, the existing methods have some issues such as feature scarcity, feature loss and excessive dependence on manual analysis. To address these issues, we propose in this paper a code visualization method with multi-channel image size adaptation (MC-ISA) that can detect large-scale samples more quickly without manual reverse analysis. Experimental results demonstrate that MC-ISA achieves both higher accuracy and F1-score than the existing B2M algorithm after introducing three mechanisms including image size adaptive, color enhancement and multi-channel enhancement.
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