Continuous authentication monitors the security of a system throughout the login session on mobile devices. In this article, we present SCANet, a two-stream convolutional neural network--based continuous authentication system that leverages the accelerometer and gyroscope on smartphones to monitor users’ behavioral patterns. We are among the first to use two streams of data—frequency domain data and temporal difference domain data—from the two sensors as the inputs of the convolutional neural network (CNN). SCANet utilizes the two-stream CNN to learn and extract representative features and then performs the principal component analysis to select the top 25 features with high discriminability. With the CNN-extracted features, SCANet exploits the one-class support vector machine to train the classifier in the enrollment phase. Based on the trained CNN and classifier, SCANet identifies the current user as a legitimate user or an impostor in the continuous authentication phase. We evaluate the effectiveness of the two-stream CNN and the performance of SCANet on our dataset and BrainRun dataset, and the experimental results demonstrate that CNN achieves 90.04% accuracy, and SCANet reaches an average of 5.14% equal error rate on two datasets and takes approximately 3 s for user authentication.

中文翻译：

---

SCANet

连续身份验证在移动设备上的整个登录会话期间监控系统的安全性。在本文中，我们介绍了 SCANet，这是一种基于双流卷积神经网络的连续身份验证系统，它利用智能手机上的加速度计和陀螺仪来监控用户的行为模式。我们是最早使用来自两个传感器的两个数据流——频域数据和时间差域数据——作为卷积神经网络 (CNN) 输入的公司之一。SCANet 利用双流 CNN 学习和提取代表性特征，然后进行主成分分析，选择具有高可区分性的前 25 个特征。借助 CNN 提取的特征，SCANet 利用一类支持向量机在注册阶段训练分类器。基于经过训练的 CNN 和分类器，SCANet 在持续认证阶段将当前用户识别为合法用户或冒名顶替者。我们在我们的数据集和 BrainRun 数据集上评估了双流 CNN 的有效性和 SCANet 的性能，实验结果表明 CNN 达到了 90.04% 的准确率，而 SCANet 在两个数据集上平均达到了 5.14% 的相等错误率，并取用户认证大约需要 3 秒。