CNN for User Activity Detection Using Encrypted In-App Mobile Data
CNN for User Activity Detection Using Encrypted In-App Mobile Data
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DOI：
10.3390/fi14020067
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发表时间：
2022-02
期刊：
影响因子：
3.4
通讯作者：
Madushi H. Pathmaperuma;Y. Rahulamathavan;Safak Dogan;A. Kondoz
中科院分区：
文献类型：
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作者：
Madushi H. Pathmaperuma;Y. Rahulamathavan;Safak Dogan;A. Kondoz
在这项研究中，提出了一个简单而有效的框架，使用卷积神经网络（CNN）来表征在移动的应用程序上执行的细粒度应用程序内用户活动。所提出的框架使用基于时间窗口的方法将活动的加密流量划分为多个段，以便仅通过观察与活动相关的加密流量的一部分来识别应用内活动。在这项研究中，矩阵构造为每个加密的交通流段。这些矩阵充当CNN模型的输入，使其能够学习区分先前训练（已知）和先前未训练（未知）的应用内活动以及已知的应用内活动类型。所提出的方法提取和选择加密流量分类的显着特征。这是第一个已知的方法，提出过滤未知流量，平均准确率为88%。一旦过滤掉未知流量，我们的模型的分类准确率将达到92%。
In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previously trained (known) and previously untrained (unknown) in-app activities as well as the known in-app activity type. The proposed method extracts and selects salient features for encrypted traffic classification. This is the first-known approach proposing to filter unknown traffic with an average accuracy of 88%. Once the unknown traffic is filtered, the classification accuracy of our model would be 92%.