信息安全研究2024，Vol.10Issue(3)：216-222,7.DOI:10.12379/j.issn.2096-1057.2024.03.04
基于GHM可视化和深度学习的恶意代码检测与分类
Malware Detection and Classification Based on GHM Visualization and Deep Learning
摘要
Abstract
Malware detection is becoming more and more challenging due to the increasing complexity and variability of malicious code.Most mutated or unknown malicious programs are formed by improving or obfuscating the logic of existing malicious codes,so it is becoming more and more important to discover malicious code families and determine their malicious behaviors.In this paper,we proposed a novel malware visualization method based on GHM(Gray,HOG,Markov)for data preprocessing.Unlike the traditional visualization methods,this method extracts more effective data features through HOG and Markov in the visualization process,and constructs a three-channel color image.In addition,a VLMal classification model based on CNN and LSTM is constructed to realize the malware detection and classification of visual images.Experimental results show that this method can effectively detect and classify malicious code with good accuracy and stability.关键词
恶意软件检测/深度学习/恶意软件分类/内存取证/可视化Key words
malware detection/deep learning/malware classification/memory forensics/visualization分类
信息技术与安全科学引用本文复制引用
张淑慧,胡长栋,王连海,徐淑奖,邵蔚,兰田..基于GHM可视化和深度学习的恶意代码检测与分类[J].信息安全研究,2024,10(3):216-222,7.基金项目
国家自然科学基金项目(62102209) （62102209）
山东省自然科学基金重点项目(ZR2020KF035) （ZR2020KF035）
泰山学者工程资助项目(tsqn202312231) （tsqn202312231）
