Research on a High Robust Detection Model for Malicious Software

doi:10.3969/j.issn.1671-1122.2024.08.005

Abstract

Abstract:

In recent years, malware has become increasingly harmful to the security of cyberspace. In order to cope with large-scale malware detection tasks in the network environment, researchers have proposed automatic detection methods based on machine learning and deep learning. However, these methods need to spend more time on feature engineering, resulting in low detection efficiency. At the same time, the existence of malware countersamples also affects these methods to make correct judgments, causing harm to information security. Therefore, this paper proposed a robust malware detection method (MDCAM). This method firstly analyzed the characteristics of different families of malware and malware adversarial examples based on code visualization technology, and then builded a detection model that integrated improved ConvNeXt network, mixed domain attention mechanism and FocalLoss function, which significantly improved the comprehensive ability and robustness of the detection model.

Key words: malware detection, deep learning, adversarial examples

CLC Number:

TP309

XU Ruzhi, ZHANG Ning, LI Min, LI Zixuan. Research on a High Robust Detection Model for Malicious Software[J]. Netinfo Security, 2024, 24(8): 1184-1195.

Figures/Tables 22

References 35

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文件大小/KB	图像宽度/像素
<10	32
10~30	64
30~60	128
60~100	256
100~200	384
200~500	512
500~1000	768
>1000	1024

卷积核大小	精确率	召回率	F1值	准确率
3?3	96.13%	97.03%	96.58%	98.30%
5?5	95.85%	96.72%	96.28%	98.20%
7?7	95.45%	96.52%	95.98%	98.16%
9?9	96.94%	94.10%	95.50%	98.25%

Stage	精确率	召回率	F1值	准确率
3,3,9,3	96.13%	97.03%	96.58%	98.30%
3,3,3	98.24%	98.31%	98.27%	98.39%
3,9,3	98.28%	98.31%	98.29%	98.43%
3,3,9	98.38%	98.16%	98.26%	98.39%
9,3,3	98.36%	98.34%	98.34%	98.53%
9,3,3+FocalLoss	98.40%	98.37%	98.38%	98.57%

位置	精确率	召回率	F1值	准确率
1	98.52%	98.32%	98.42%	98.57%
2	98.55%	98.25%	98.40%	98.62%
3	98.65%	98.38%	98.51%	98.76%

方法	精确率	召回率	F1值	准确率	Flops/次
Baseline	98.40%	98.37%	98.38%	98.57%	4.7395
Baseline+SE	95.97%	96.61%	96.26%	98.25%	4.7507
Baseline+CA	98.37%	96.83%	97.59%	98.39%	4.7540
Baseline +CBAM	98.65%	98.38%	98.51%	98.76%	5.1865
MDCAM(本文)	98.71%	98.62%	98.67%	98.76%	4.7589