Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao 56000, Thailand.
Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand.
Sensors (Basel). 2021 Nov 12;21(22):7519. doi: 10.3390/s21227519.
Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework.
智能手机作为无处不在的小工具，随着传感器、网络和处理能力的进步，正在变得越来越智能化和上下文感知化。这些设备为用户提供了一个全面的平台，可以随时随地进行社交、交流、收发电子邮件以及存储和访问个人数据等活动。如今，智能手机被用于存储大量的私人和敏感数据，包括银行账户信息、个人标识符、账户密码和信用卡信息。许多用户保持永久登录状态，因此，他们的移动设备容易受到犯罪分子攻击带来的安全和隐私风险。密码、个人识别码、图案锁、面部验证和指纹扫描都容易受到各种攻击，包括污迹攻击、旁路攻击和肩窥攻击。为了解决这些问题，本研究提出了一种名为 DeepAuthen 的新的连续认证框架，该框架基于智能手机上的加速度计、陀螺仪和磁力计传感器测量的用户的身体活动模式来识别智能手机用户。我们在三个基准数据集 UCI-HAR、WISDM-HARB 和 HMOG 上使用几种深度学习分类器，包括我们提出的深度学习网络 DeepConvLSTM，对用户认证进行了一系列测试。结果表明，结合各种运动传感器数据可以获得最高的准确率和能量效率比（EER）值用于二进制分类。我们还对连续认证结果进行了全面检查，结果支持了我们框架的有效性。
