基于卷积神经网络的恶意代码动态检测模型构建  

Construction of a Dynamic Malware Detection Model Based on Convolutional Neural Networks

作　　者：路红 韩运宝 贾晏瑶 LU Hong;HAN Yunbao;JIA Yanyao(China Software Evaluation Center,Beijing 100048)

机构地区：[1]中国软件评测中心,北京100048

出　　处：《软件》2025年第3期97-100,共4页Software

摘　　要：随着互联网的普及和信息技术的飞速进步,恶意代码带来的威胁愈发严峻。为了有效应对这一问题,基于卷积神经网络的恶意代码动态检测模型逐渐成为研究热点。本文提出了一种基于卷积神经网络的恶意代码动态检测模型,该模型能够高效、精准地识别恶意代码,为安全防护提供了可行的解决方案。With the popularization of the Internet and the rapid progress of information technology,the threat brought by malicious code becomes more and more serious.In order to effectively address this issue,dynamic detection models for malicious code based on convolutional neural networks have gradually become a research hotspot.This article proposes a dynamic detection model for malicious code based on convolutional neural networks,which can efficiently and accurately identify malicious code and provide a feasible solution for security protection.

关 键 词：恶意代码 动态检测 卷积神经网络 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]