Adaptive Deep Feature Fusion for Continuous Authentication With Data Augmentation
Adaptive Deep Feature Fusion for Continuous Authentication With Data Augmentation
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DOI：
10.1109/tmc.2022.3186614
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发表时间：
2023-10
影响因子：
7.9
通讯作者：
Yantao Li;Li Liu;Huafeng Qin;Shaojiang Deng;M. El-Yacoubi;Gang Zhou
中科院分区：
文献类型：
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作者：
Yantao Li;Li Liu;Huafeng Qin;Shaojiang Deng;M. El-Yacoubi;Gang Zhou
移动的设备正变得越来越流行，并在我们的日常生活中扮演着重要的角色。然而，安全性不足和保护机制薄弱导致无人值守设备的隐私泄露严重。为了充分保护移动终端的隐私，我们提出了ADFFDA，一种新的移动的连续认证系统，使用自适应深度特征融合方案进行有效的特征表示，并通过利用智能手机内置的加速度计，陀螺仪和磁力计的传感器，基于变换的GAN进行数据增强。给定归一化的传感器数据，ADFFDA利用基于transformer的GAN（由基于transformer的生成器和基于CNN的训练器组成）来增强CNN训练的训练数据。ADFFDA利用增强后的数据和专门设计的基于ghost模块和ghost瓶颈的CNN，通过训练好的CNN从三个传感器中提取深度特征，并利用自适应加权级联方法对CNN提取的特征进行自适应融合。ADFFDA在融合特征的基础上，使用单类SVM（OC-SVM）分类器对用户进行身份认证。我们从基于变换器的GAN、基于GAN的数据增强、CNN架构、自适应加权特征融合、OC-SVM分类器和安全分析的效率方面评估了ADFFDA的认证性能。实验结果表明，ADFFDA获得了最好的认证性能w.r.t代表性的方法，通过实现0.01%的平均等错误率。
Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive Deep Feature Fusion scheme for effective feature representation, and a transformer-based GAN for Data Augmentation, by leveraging smartphone built-in sensors of the accelerometer, gyroscope and magnetometer. Given the normalized sensor data, ADFFDA utilizes the transformer-based GAN consisting of a transformer-based generator and a CNN-based discriminator to augment the training data for CNN training. With the augmented data and the especially-designed CNN based on the ghost module and ghost bottleneck, ADFFDA extracts deep features from the three sensors by the trained CNN, and exploits an adaptive-weighted concatenation method to adaptively fuse the CNN-extracted features. Based on the fused features, ADFFDA authenticates users by using the one-class SVM (OC-SVM) classifier. We evaluate the authentication performance of ADFFDA in terms of the efficiency of the transformer-based GAN, GAN-based data augmentation, CNN architecture, adaptive-weighted feature fusion, OC-SVM classifier, and security analysis. The experimental results show that ADFFDA obtains the best authentication performance w.r.t representative approaches, by achieving a mean equal error rate of 0.01%.