信息安全研究2025，Vol.11Issue(1)：28-34,7.DOI:10.12379/j.issn.2096-1057.2025.01.05
基于位图表征与U-Att分类网络的恶意软件识别技术
Malware Identification Technology Based on Bitmap Representation and U-Att Classification Network
屈梦楠 1靳宇浩 1张光华1
作者信息
- 1. 河北科技大学信息科学与工程学院 石家庄 050018
- 折叠
摘要
Abstract
In the field of computer security,malware identification has always been a challenging task.The current malware detection technology based on deep learning has many problems such as insufficient generalization ability and high performance loss.To surmount these obstacles,this paper introduces an innovative technique predicated upon bitmap representation coupled with a U-Att classification network for the discernment of malicious software.This technique augments the residual U-Net architecture with an integrated attention mechanism,culminating in the U-Att classification network that exhibits adaptive focusing on salient regions of malicious samples,thereby ameliorating classification efficacy.Comprehensive validation through the utilization of various public datasets ensued,accompanied by a comparative analysis against alternative methodologies.The empirical findings substantiate the network's superior performance within the context of malware identification tasks.关键词
恶意软件识别/图像处理/残差U-Net网络/注意力机制Key words
malware identification/image processing/residual U-Net network/attention mechanism分类
计算机与自动化引用本文复制引用
屈梦楠,靳宇浩,张光华..基于位图表征与U-Att分类网络的恶意软件识别技术[J].信息安全研究,2025,11(1):28-34,7.
