基于位图表征与U-Att分类网络的恶意软件识别技术OA北大核心

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.