一种基于改进深度残差收缩网络的恶意应用检测方法
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作者单位:

1.南京信息工程大学 电子与信息工程学院;2.南京信息工程大学 电子与信息工程学院

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基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


A Malicious Application Detection Method Based on Improved Depth Residual Shrinkage Network
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Affiliation:

1.School of Electronic & Information Engineering ,Nanjing University of Information Science & Technology;2.School of Electronic & Information Engineering ,Nanjing University of Information Science & Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    恶意应用的快速增长给移动智能终端带来了巨大的安全威胁,实现恶意应用高精度检测对移动网络信息安全具有重要意义。本文提出一种基于改进深度残差收缩网络的恶意应用检测方法。首先将流量特征预处理成卷积神经网络输入,接着引入通道注意力机制和空间注意力机制,从通道和空间两个维度对样本特征进行加权。然后再引入深度残差收缩网络,自适应滤除样本冗余特征并通过恒等连接优化参数反向传播,减小模型训练和分类的难度,最终实现安卓恶意应用高精度识别。所提方法可避免手工提取特征,能实现高精度分类并且具有一定泛化能力。实验结果表明,所提方法在恶意应用的2分类、4分类和42分类中准确率分别为99.40%、99.95%和97.33%,与现有方法相比,具有较高的分类性能与泛化能力。

    Abstract:

    The rapid growth of malicious applications has brought a huge security threat to mobile intelligent terminals. It is of great significance to achieve high-precision detection of malicious applications for mobile network information security. In view of this, this paper proposes a malicious application detection method based on improved deep residual shrinkage network. Firstly, the traffic features are preprocessed into convolutional neural network inputs, and then the channel attention mechanism and spatial attention mechanism are introduced to weight the sample features from the channel and spatial dimensions. Then, the deep residual shrinkage network is introduced to adaptively filter out the redundant features of the samples, and the parameters are back propagated through the identical connection optimization, so as to reduce the difficulty of model training and classification, and finally realize the high-precision identification of Android malicious applications. The proposed method can avoid manual feature extraction, achieve high-precision classification and has certain generalization ability. Experimental results show that the accuracy of the proposed method is 99.40%, 99.95% and 97.33% in 2-classification, 4 -classification and 42-classification of malicious applications, which is 0.21%, 2.65% and 25.85% higher than the existing methods.Experimental results show that the accuracy of the proposed method in 2-classification, 4-classification and 42-classification of malicious applications is 99.40%, 99.95% and 97.33% respectively. Compared with the existing methods, the proposed method has higher classification performance and generalization ability.

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  • 收稿日期:2021-07-04
  • 最后修改日期:2021-10-28
  • 录用日期:2021-12-09
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