Abstract
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.
Highlights
Advances in wearable technology are progressing at incredible speeds, generating considerable interest in both professional and research communities
This study evaluated the proposed DeepAuthen framework against basic deep learning algorithms using three public datasets, namely UCI-human activity recognition (HAR), WISDM-HARB, and HMOG
The following subsections provide the experimental observations of these deep learning methods trained on mobile sensing data on various datasets
Summary
Advances in wearable technology are progressing at incredible speeds, generating considerable interest in both professional and research communities. Smartphones, tablets, smartwatches, smart shoes, clothing, and other smart wearables are incorporating an increasing amount of extra processing capability and sensors. These augmented items serve as facilitators of ubiquitous computing, gathering data that can be utilized to offer wearers different digital solutions [1]. Numerous modern smart wearables have integrated accelerometers, gyroscopes, and magnetometers to record body motion [2,3,4]. Smartphones have become the custodians of human personal data including medical data (e.g., heart rate, vaccination, and other medical treatment history), bank account information and personalized credentials for various applications and services. Users have recently begun to express concerns about the confidentiality of their personal
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