Kılıç Kazım, Doğru İbrahim Alper, Toklu Sinan
IoTLab, Department of Computer Engineering, Gazi University, Ankara, Turkey.
Information Technology Faculty, Mingachevir State University, Mingeçevir, Mingeçevir, Azerbaijan.
PeerJ Comput Sci. 2024 Oct 17;10:e2362. doi: 10.7717/peerj-cs.2362. eCollection 2024.
The Android operating system holds the vast majority of the market share in smart device usage worldwide. The Android operating system, which is of interest to users, is increasing its usage rate day by day due to its open source nature and free applications. Applications can be installed on the Android operating system from official application markets and unofficial third-party environments, which poses a great risk to users' privacy and security.
In this study, an attention-layered mini-ResNet model is proposed, which can detect QR code-like images created using the 100 most effective defined permission information of Android applications. In the proposed method, permission information is obtained from four different datasets with different number of applications. QR code-like images of size 10x10x1 are created by selecting effective permissions using the chi-square technique. In the proposed classification architecture, residual layers are used to avoid ignoring the residual features of the images, and attention layers are used to focus on specific regions after each residual layer. The proposed architecture has a low number of parameters and memory consumption despite adding the residual layer and the weighting operations in the attention layer.
Using the proposed method, accuracy values of 96.95%, 98.34%, 98.33% and 100% were achieved, respectively, on four datasets containing applications obtained from different sources such as Androzoo, Drebin, Genome and Google Play Store. On the Mix dataset, which is a combination of four datasets, an accuracy value of 96.7% was produced with the proposed method. When 10-fold cross validation was applied to reduce the suggested bias, accuracy values of 97.50%, 98.62%, 98%, 94% and 97.61% were obtained, respectively. The success and durability of the proposed method in different environments have been tested through experiments conducted on different datasets. The results show that the proposed method exhibits better classification performance compared to classical machine learning algorithms, deep learning-based studies using permission information, and similar image-based studies.
安卓操作系统在全球智能设备使用中占据绝大多数市场份额。受用户关注的安卓操作系统，因其开源特性和免费应用，其使用率日益增加。应用程序可从官方应用市场和非官方第三方环境安装到安卓操作系统上，这对用户隐私和安全构成巨大风险。
在本研究中，提出了一种注意力分层的迷你残差网络模型，该模型能够检测使用安卓应用100个最有效的定义权限信息创建的类似二维码的图像。在所提出的方法中，权限信息从四个包含不同数量应用程序的不同数据集中获取。通过使用卡方技术选择有效权限，创建大小为10x10x1的类似二维码的图像。在所提出的分类架构中，使用残差层以避免忽略图像的残差特征，并在每个残差层之后使用注意力层来聚焦特定区域。尽管在注意力层中添加了残差层和加权操作，但所提出的架构具有较少的参数数量和内存消耗。
使用所提出的方法，在包含从不同来源（如Androzoo、Drebin、Genome和谷歌应用商店）获取的应用程序的四个数据集上，分别取得了96.95%、98.34%、98.33%和100%的准确率值。在由四个数据集组合而成的混合数据集上，所提出的方法产生了96.7%的准确率值。当应用10折交叉验证以减少所建议的偏差时，分别获得了97.50%、98.62%、98%、94%和97.61%的准确率值。通过在不同数据集上进行的实验，测试了所提出的方法在不同环境中的成功率和耐久性。结果表明，与经典机器学习算法、使用权限信息的基于深度学习的研究以及类似的基于图像的研究相比，所提出的方法表现出更好的分类性能。
