Behavioral biometrics-based continuous authentication is a promising authentication scheme, which uses behavioral biometrics recorded by built-in sensors to authenticate smartphone users throughout the session. However, current continuous authentication methods suffer some limitations: 1) behavioral biometrics from impostors are needed to train continuous authentication models. Since the distribution of negative samples from diverse attackers are unknown, it is a difficult problem to solve in real-world scenarios; 2) most deep learning-based continuous authentication methods need to train two models to improve authentication performance. A deep learning model for deep feature extraction, and a machine learning-based classifier for classification; 3) weak capability of capturing users' behavioral patterns leads to poor authentication performance. To solve these issues, we propose a relative attention-based one-class adversarial autoencoder for continuous authentication of smartphone users. First, we propose a one-class adversarial autoencoder to learn latent representations of legitimate users' behavioral patterns, which is trained only with legitimate smartphone users' behavioral biometrics. Second, we present the relative attention layer to capture richer contextual semantic representation of users' behavioral patterns, which modifies the standard self-attention mechanism using convolution projection instead of linear projection to perform the attention maps. Experimental results demonstrate that we can achieve superior performance of 1.05% EER, 1.09% EER, and 1.08% EER with a high authentication frequency (0.7s) on three public datasets.

翻译：基于行为生物特征的持续认证是一种有前景的认证方案，它利用内置传感器记录的行为生物特征在整个会话期间对智能手机用户进行认证。然而，当前的持续认证方法存在一些局限性：1）训练持续认证模型需要来自冒名顶替者的行为生物特征。由于来自不同攻击者的负样本分布未知，这在现实场景中是一个难以解决的问题；2）大多数基于深度学习的持续认证方法需要训练两个模型以提高认证性能，即一个用于深度特征提取的深度学习模型和一个基于机器学习的分类器用于分类；3）捕获用户行为模式的能力较弱导致认证性能不佳。为解决这些问题，我们提出了一种基于相对注意力的单类对抗自编码器用于智能手机用户的持续认证。首先，我们提出了一种单类对抗自编码器来学习合法用户行为模式的潜在表示，该模型仅使用合法智能手机用户的行为生物特征进行训练。其次，我们提出了相对注意力层以捕获用户行为模式更丰富的上下文语义表示，该层通过使用卷积投影而非线性投影来生成注意力图，从而改进了标准的自注意力机制。实验结果表明，在三个公开数据集上，我们能够以高认证频率（0.7秒）实现1.05% EER、1.09% EER和1.08% EER的优异性能。