Malicious code classification method based on improved MobileNetV2

doi:10.11772/j.issn.1001-9081.2022060931

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2217-2225.DOI: 10.11772/j.issn.1001-9081.2022060931

Special Issue: 网络空间安全

• Cyber security • Previous Articles Next Articles

Malicious code classification method based on improved MobileNetV2

Bona XUAN, Jin LI(), Yafei SONG, Zexuan MA

Air and Missile Defense College，Air Force Engineering University，Xi’an Shaanxi 710051，China

Received:2022-06-28 Revised:2022-08-27 Accepted:2022-09-05 Online:2022-11-25 Published:2023-07-10
Contact: Jin LI
About author:XUAN Bona， born in 1991， M. S. candidate. Her research interests include network security and defense， malware code classification.
LI Jin， born in 1971， Ph. D.， associate professor. His research interests include network security of ground to air missile command control system.
SONG Yafei， born in 1987， Ph. D.， associate professor. His research interests include intelligent information processing.
MA Zexuan， born in 1998， M. S. candidate. His research interests include network intrusion detection.
Supported by:
National Natural Science Foundation of China(61876189)

基于改进MobileNetV2的恶意代码分类方法

轩勃娜, 李进(), 宋亚飞, 马泽煊

空军工程大学防空反导学院，西安 710051

通讯作者: 李进
作者简介:轩勃娜（1991—），女，陕西咸阳人，硕士研究生，主要研究方向：网络安全防御、恶意代码分类；
李进（1971—），男，陕西西安人，副教授，博士，主要研究方向：地空导弹指挥控制系统网络安全；
宋亚飞（1987—），男，河南汝州人，副教授，博士，主要研究方向：智能信息处理；
马泽煊（1998—），男，河北保定人，硕士研究生，主要研究方向：网络入侵检测。
基金资助:
国家自然科学基金资助项目(61876189)

Abstract

Abstract:

Aiming at the problems of insufficient accuracy， high prediction time cost and weak ability against obfuscation of the traditional malicious code classification methods， a malicious code classification method based on improved MobileNetV2 was proposed. Firstly， aiming at the problems of malicious code encryption and obfuscation， the Coordinate Attention （CA） method was used to introduce a wider range of spatial locations to enhance malicious code image features. Then， aiming at the problem of high training cost caused by training from scratch， the Transfer Learning （TL） was used to improve the MobileNetV2 learning method to increase the ability against obfuscation. Finally， aiming at the large computational load and slow convergence of traditional deep learning networks， the MobileNetV2 lightweight convolutional network model was used， and Ranger21 was combined to improve the training method to promote rapid convergence. Experimental results show that the above-mentioned method has the accuracy achieved 99.26% and 96.98% for Malimg dataset and DataCon dataset. The method has the accuracy increased by 1.49% and the detection efficiency increased by 45.31% on the Malimg dataset compared with the AlexNet method， and has the accuracy increased by 1.14% on the DataCon dataset compared with the ensemble learning method. It can be seen that the improved MobileNetV2 based malicious code classification method can improve the generalization ability， ability against obfuscation and classification efficiency of the model.

Key words: network security, malicious code classification, Transfer Learning (TL), MobileNetV2, Coordinate Attention (CA), Ranger21 optimization algorithm

摘要：

针对传统恶意代码分类方法存在的精度不足、预测时间成本高和抗混淆能力弱等问题，提出一种基于改进MobileNetV2的恶意代码分类方法。首先，针对恶意代码加密和混淆等问题，使用坐标注意力（CA）方法引入更大范围的空间位置来增强恶意代码图像的特征；然后，针对从头开始训练导致的训练成本过高的问题，使用迁移学习（TL）来改进MobileNetV2的学习方式以提升抗混淆能力；最后，针对传统深度学习网络计算量大和收敛慢的问题，使用MobileNetV2轻量化卷积网络模型，并结合Ranger21改进训练方式以促进网络迅速收敛。实验结果表明：上述方法对Malimg数据集和DataCon数据集的准确率分别达到了99.26%和96.98%。在malimg数据集相较于AlexNet方法在准确率上平均提升了1.49%，检测效率上平均提升了45.31%；在DataCon数据集相较于集成学习方法准确率平均提升了1.14%。可见，基于改进MobileNetV2的恶意代码分类方法可以提升模型的泛化能力、抗混淆能力与分类效率。

关键词: 网络安全, 恶意代码分类, 迁移学习, MobileNetV2, 坐标注意力, Ranger21优化算法

CLC Number:

TP309.5

Bona XUAN, Jin LI, Yafei SONG, Zexuan MA. Malicious code classification method based on improved MobileNetV2[J]. Journal of Computer Applications, 2023, 43(7): 2217-2225.

轩勃娜, 李进, 宋亚飞, 马泽煊. 基于改进MobileNetV2的恶意代码分类方法[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2217-2225.

Figures/Tables 19

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输入长度	操作	e	c	n	s
2 562×3	Conv2d	—	16	1	2
1 122×16	Bottleneck	1	16	1	2
562×16	Bottleneck	3	24	1	2
282×24	Bottleneck	3	24	2	1
142×40	Bottleneck	6	48	4	1
72×96	Bottleneck	6	96	2	2
72×96	Conv2d，1×1	6	576	1	1
72×576	Avgpool 7×7	—	—	1	1
72×576	Conv2d，1×1	—	1 028	1	1
8×1×256	Avgpool 8×8	—	1 028	1	1
8×1×256	Avgpool 8×8	—	1 028	1	1
16×1×256	Concate	—	1 028	1	1
16×1×256	Conv2d，1×1	—	1 028	1	1
16×1×40	BatchNorm+Relu	—	1 028	1	1
12×1 280	Multiply，8×8	—	1 028	1	1
12×1 280	Conv2d，1×1	—	1 028	1	1
12×1 280	Dropout	—	1 028	1	1
12×1 280	Conv2d，1×1	—	class	1	1

输入长度	操作	e	c	n	s
2 562×3	Conv2d	—	16	1	2
1 122×16	Bottleneck	1	16	1	2
562×16	Bottleneck	3	24	1	2
282×24	Bottleneck	3	24	2	1
142×40	Bottleneck	6	48	4	1
72×96	Bottleneck	6	96	2	2
72×96	Conv2d，1×1	6	576	1	1
72×576	Avgpool 7×7	—	—	1	1
72×576	Conv2d，1×1	—	1 028	1	1
8×1×256	Avgpool 8×8	—	1 028	1	1
8×1×256	Avgpool 8×8	—	1 028	1	1
16×1×256	Concate	—	1 028	1	1
16×1×256	Conv2d，1×1	—	1 028	1	1
16×1×40	BatchNorm+Relu	—	1 028	1	1
12×1 280	Multiply，8×8	—	1 028	1	1
12×1 280	Conv2d，1×1	—	1 028	1	1
12×1 280	Dropout	—	1 028	1	1
12×1 280	Conv2d，1×1	—	class	1	1

样本大小/KB	宽度/像素
（0，6］	32
（6， 20］	64
（20， 46］	128
（46， 80］	256
（80，160］	384
（160， 800］	512
（800， 1 200］	1 024
（1 200，76 900］	2 048

样本大小/KB	宽度/像素
（0，6］	32
（6， 20］	64
（20， 46］	128
（46， 80］	256
（80，160］	384
（160， 800］	512
（800， 1 200］	1 024
（1 200，76 900］	2 048

图片尺寸/像素	准确率/%	召回率/%	精确率/%	F₁/%
32×32	93.14	92.49	93.00	93.36
64×64	93.68	93.01	93.13	93.08
128×128	94.84	94.01	94.09	94.28
256×256	96.98	96.94	95.88	96.69

Malicious code classification method based on improved MobileNetV2

基于改进MobileNetV2的恶意代码分类方法

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Figures/Tables 19

References 25

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Metrics

模型	训练方式	准确率
Resnet50	预训练	91.90
Resnet50	微调	94.40
Destnet169	预训练	93.41
Destnet169	微调	94.61
InceptionV3	预训练	91.41
InceptionV3	微调	92.88
MobileNetV2	预训练	92.97
MobileNetV2	微调	95.30
本文模型	预训练	94.39
本文模型	微调	96.98

方法	评价参数/%				网络深度	参数量	图片尺寸/像素
方法	准确率	召回率	精确率	F₁	网络深度	参数量	图片尺寸/像素
TL-Resnet50	94.40	94.40	94.67	94.22	168	25 636 712	256×256
TL-Destnet169	94.61	94.61	94.31	94.53	169	14 307 880	256×256
TL-InceptionV3	92.88	92.48	92.61	92.51	126	22 910 480	256×256
TL-MobileNetV2	95.30	95.23	95.41	95.28	88	2 259 265	256×256
CATM	96.98	96.94	95.88	96.69	92	2 357 984	256×256

优化器	准确率	召回率	精确率	F₁
CATM-Adam	96.66	96.21	95.58	95.86
CATM-Ranger21	96.98	96.94	95.88	96.69

方法	特征	准确率/%	召回率/%	精确率/%	F₁/%	耗时/ms	同一个家族的变种准确率/%
方法	特征	准确率/%	召回率/%	精确率/%	F₁/%	耗时/ms	Swizzor.gen！E	Swizzor.gen！I
文献［5］方法	灰度图	94.50	94.50	94.60	94.50	20.3	71.00	62.00
文献［6］方法	灰度图	98.48	96.56	95.80	95.80	—	88.00	82.00
文献［8］方法	直方图+GLCM	98.58	98.06	98.04	98.05	—	—	—
文献［7］方法	灰度图	98.27	98.25	98.19	98.20	—	88.00	82.00
文献［7］方法	RGB	98.82	98.85	98.81	98.75	—	85.00	74.00
文献［25］方法	AlexNet+RGB	97.80	97.80	98.00	97.80	9.3	97.00	80.00
本文方法	灰度图	99.26	98.99	99.14	99.11	6.4	90.00	86.00

方法	特征	准确率/%	召回率/%	精确率/%	F₁/%	耗时/ms	图片尺寸/像素
文献［9］方法	多种特征	95.89	89.84	—	88.55	—	256×256
DataCon比赛^［10］方法	灰度图	96.80	96.42	96.26	97.38	—	512×512
本文方法	灰度图	96.98	96.94	95.88	96.69	7.2	256×256

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