基于随机平滑的恶意软件识别深度学习模型鲁棒性认证方法

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doi:10.15918/j.tbit1001-0645.2022.044

bob手机在线登陆信息与电子学院，北京　100081

基金项目:工信部2020年信息安全软件项目（CEIEC-2020-ZM02-0134）

详细信息

作者简介:
罗森林（1968—），男，教授，博士生导师，E-mail：luosenlin@bit.edu.cn

通讯作者:
潘丽敏（1968—），女，硕士，高级实验师，E-mail：panlimin2016@gmail.com

中图分类号:TP181
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- 文章访问数:72
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- 被引次数:0
出版历程
- 收稿日期:2022-03-02
- 录用日期:2022-05-30

Certified Robustness of Malware Deep Learning Identification Model Based on Random Smoothing

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

摘要

摘要:面向恶意软件识别的任务中，使用随机平滑算法向所有特征中添加噪声得到的噪声样本可能会失去恶意功能. 现有的认证算法按照噪声空间分布的似然比从大到小的顺序构建认证区域，使认证的鲁棒区域小、认证准确率低. 本文提出一种基于随机平滑的恶意软件识别深度学习模型的鲁棒性认证方法，方法只向与恶意功能非必需的特征中添加离散伯努利噪声构建可认证的平滑模型，选取似然比更小的区域构建认证区域，实现更准确的鲁棒性认证. 实验表明，提出的方法在3个数据集上平均认证半径是对比方法的 4.37倍、2.67倍和2.72倍。该方法可以提供与实际鲁棒边界更紧密的认证半径，在模型鲁棒性评估方面具有较强的实用价值.
- 鲁棒性认证/
- 随机平滑/
- 恶意软件
Abstract:Robustness, the ability to resist uncertain disturbances, is an important index of machine learning model. The certified method based on random smoothing can certify the robustness of large and complex models. In the task of malware identification, the noise samples obtained by adding noise to all features using random smoothing algorithm may lose the malicious function. The existing certification algorithms construct the certified region according to the likelihood ratio of noise spatial distribution from large to small, causing the certified robust region small and the certified accuracy not good. So, a robust certification method was proposed based on random smoothing for malware recognition deep learning model. The method was arranged to add discrete Bernoulli noise only to the unnecessary features of malicious functions to construct a certifiable smoothing model, and to select the region with smaller likelihood ratio to construct a certified region to achieve more accurate certified robustness. Experiment results show that the average certified radius of the proposed method on three data sets is 4.37 times, 2.67 times and 2.72 times that of the comparison method. This method can provide the certified radius closer to the actual robust boundary, possessing a strong practical value in the evaluation of model robustness.
- certified robustness/
- random smoothing/
- malware

HTML全文

图 1恶意软件识别深度学习模型鲁棒性认证原理框架

Figure 1.Principle framework of robustness certificate for the malware identification deep learning model

下载: 全尺寸图片幻灯片

图 2不同认证方法的认证准确率

Figure 2.Certified accuracy of models with different certificate methods

下载: 全尺寸图片幻灯片

表 1实现恶意功能的必需权限及示例

Table 1.Sensitive permission categories and examples

相关权限	权限示例
传感器相关	BODY_SENSORS
相机相关	CAMERA
通话相关	READ_CALL_LOG
短信相关	READ_SMS
音频相关	RECORD_AUDIO
日历相关	WRITE_CALENDAR
联系人相关	WRITE_CONTACTS
系统软件权限	MOUNT_FORMAT_FILESYSTEMS
内存读写相关	READ_EXTERNAL_STORAGE
位置相关	ACCESS_FINE_LOCATION

下载: 导出CSV

表 2不同认证算法认证的平均认证半径

Table 2.Average certified radius of different certificate algorithms

数据集	认证算法
数据集	JIA	COHEN	LECUYER	本文
Drebin	1.42	0.00	0.00	6.20
Androzoo	1.02	0.00	0.00	2.74
CIC	1.40	0.00	0.00	3.81

下载: 导出CSV

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