恶意软件检测和分类可视化技术综述

doi:10.11959/j.issn.2096-109x.2023064

Abstract

Abstract:

With the rapid advancement of technology, network security faces a significant challenge due to the proliferation of malicious software and its variants.These malicious software use various technical tactics to deceive or bypass traditional detection methods, rendering conventional non-visual detection techniques inadequate.In recent years, data visualization has gained considerable attention in the academic community as a powerful approach for detecting and classifying malicious software.By visually representing the key features of malicious software, these methods greatly enhance the accuracy of malware detection and classification, opening up extensive research opportunities in the field of cyber security.An overview of traditional non-visual detection techniques and visualization-based methods were provided in the realm of malicious software detection.Traditional non-visual approaches for malicious software detection, including static analysis, dynamic analysis, and hybrid techniques, were introduced.Subsequently, a comprehensive survey and evaluation of prominent contemporary visualization-based methods for detecting malicious software were undertaken.This primarily encompasses encompassed the integration of visualization with machine learning and visualization combined with deep learning, each of which exhibits distinct advantages and characteristics within the domain of malware detection and classification.Consequently, the holistic consideration of several factors, such as dataset size, computational resources, time constraints, model accuracy, and implementation complexity, is necessary for the selection of detection and classification methods.In conclusion, the challenges currently faced by detection technologies are summarized, and a forward-looking perspective on future research directions in the field is provided.

Key words: machine learning, deep learning, data visualization, malware detection and classification

CLC Number:

TP309

Jinwei WANG, Zhengjia CHEN, Xue XIE, Xiangyang LUO, Bin MA. Review of malware detection and classification visualization techniques[J]. Chinese Journal of Network and Information Security, 2023, 9(5): 1-20.

Figures/Tables 15

References 74

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文献与年份	静态/动态/混合	特征	机器学习算法	分类	数据集
2001年^[1]	静态	DLL、字符串序列、字节序列	NB	机器学习	自定义数据集
2004年^[6]	静态	字节序列	NB、DT、SVM	机器学习	SourceForge、VX Heavens
2006年^[7]	静态	字节序列	NB、DT、SVM	机器学习	SourceForge、VX Heavens
2009年^[10]	静态	可变长指令序列	DT、RF	机器学习	VX Heavens
2010年^[15]	动态	稀疏向量模型	KNN、NB、J48 DT、SVM	机器学习	自定义数据集
2011年^[16]	动态	行为信息	—	—	自定义数据集
2011年^[18]	动态	指令轨迹	SVM	机器学习	自定义数据集
2013年^[11]	静态	函数调用图细粒度	KNN、SVM	机器学习	Offensive Computing
2013年^[21]	混合	函数长度频率、字符串信息矢量化、API	SVM、IB1、DT、RF	机器学习	自定义数据集
2016年^[4]	静态	API	SAE	深度学习	Comodo Cloud、SecurityCenter
2017年^[13]	静态	PE头、熵、DLL	SVM	机器学习	WINE
2019年^[14]	静态	PE头	DT、RF、KNN、LR、NB等	机器学习	VirusShare
2020年^[23]	混合	静态签名、样本灰度图、行为路径树	KNN、LMNN	机器学习	VirusSign、MalShare等
2021年^[20]	动态	行为信息、API	BGRU	深度学习	VirusShare

文献与年份	图像类型	特征	机器学习算法	分类准确率/检测准确率	数据集
2011年^[25]	灰度	GIST	KNN	98%/-	自定义数据集
2012年^[37]	热图	内核	SVM	-/98.07%	Offensive Computing
2013年^[39]	灰度	图像强度、小波与Gabor特征	SVM	-	Offensive Computing
2017年^[33]	灰度	操作码序列、函数频率	DT、LR 、NB、KNN、RF	98.9%/-	ESET NOD32/VX Hea-vens收集
2018年^[34]	彩色	纹理特征、颜色特征、字节序列	RF、KNN、SVM	97.47%/-	自定义数据集
2018年^[30]	灰度	GIST、DSIFT或LBP	KNN、RF	NDB：95.3%/-	Natarj/Antiy Databases
				Antiy：94.98%/-	(NDB和Antiy)
2018年^[31]	灰度	GIST、SIFT、GLCM等	RF、XGBoost、SVM	-	BIG2015
2019年^[32]	灰度	GIST和DSIFT	KNN、SVM、NB	98%/-	Malimg
2019年^[35]	灰度	操作码序列和纹理特征	MLP、LR、RF	85%/-	malwaredb
2020年^[38]	灰度	HOG	深度森林	96%/-	BIG2015
2020年^[36]	灰度	GLCM、操作码序列、灰度直方图	RF	97.04%/-	BIG2015

文献与年份	图像类型	特征	深度学习模型	分类准确率/检测准确率	数据集
2018年^[54]	灰度	操作码序列	MalNet (CNN (BaseNet、VGGNet)、LSTM)	99.8%/99.36%	BIG2015
2018年^[43]	灰度	-	VGG16改进	Malimg：98.52%/-	Malimg、BIG2015
				BIG2015：99.97%/-
2019年^[55]	灰度	汇编指令词向量	Lenet5改进	98.56%/-	BIG2015
2019年^[56]	灰度	字节序列，注意力图	添加注意力机制的自定义CNN	-	VX Heaven
2020年^[45]	彩色	-	VGG16改进	96.16%/-	Malimg
2020年^[47]	彩色	彩色映射	VGG16	Malimg：98.82%/-	Malimg
				IoT-android:97.35%/-	、IoT- android mobile dataset
2021年^[51]	彩色	操作码频率	RNN、自定义CNN	98.8%/-	BIG2015
2021年^[58]	彩色	操作码频率、空间填充曲线	VGG16	98.50%/-	BIG2015
2021年^[59]	彩色	空间填充曲线	VGG19	98.24%/99.02%	Kaspersky
2021年^[48]	彩色	-	Alexnet	97.8%/-	Malimg
2021年^[60]	彩色	区段特征	DSCAM	98.38%/-	VirusShare
2021年^[62]	Markov图像	纹理特征	DenseNet改进	Malimg：99.94%/-	Malimg、BIG2015
				BIG2015：98.98%/-
2021年^[64]	灰度	-	ANN	99.13%%/-	Malimg
2022年^[67]	彩色	-	Mal-Detect(DCNN、DGAN)	96.77%/-	MaleVis、Mallmg 和Virushare
2023年^[41]	彩色	二进制和汇编信息、API信	空洞空间金字塔池化改进的	Kaggle：99.48%	Kaggle、DataCon
		息间的语义关系	CNN	DataCon：97.78%

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