Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored password s. As a result of this potential scenario, this work demonstrates the possibilities of legitimate user identification in a semi-controlled environment through the built-in smartphones motion dynamics captured by two different sensors. This is a two-fold process: sub-activity recognition followed by user impostor identification. Prior to the identification; Extended Sammon Projection (ESP) method is used to reduce the redundancy among the features. To validate the proposed system, we first collected data from four users walking with their device freely placed in one of their pants pockets. Through extensive experimentation, we demonstrate that together time and frequency domain features optimized by ESP to train the wavelet kernel based extreme learning machine classifier is an effective system to identify the legitimate user or an impostor with (97 ) accuracy.
The objective of this work is to provide convenience in using smartphones differently from explicit identification, by using sensor data information. Therefore, hereby we present some key related works on this field which can be categorized into two groups: implicit user identification and multiple modality biometrics. Shi, et al @cite_9 proposed a Sen-guard method for user identification, offering continuous and implicit user identification service for smartphone users. This method leverages the sensors available on smartphones, e.g. voice, multi-touch and location; these sensors are processed together in order to get the user's identification features implicitly. Explicit identification is performed only when there is an important evidence of change in the user activity, which is not real-time upto some extent.
@cite_9
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User identification and access control have become a high demand feature on mobile devices because those devices are wildly used by employees in corporations and government agencies for business and store increasing amount of sensitive data. This paper describes SenGuard, a user identification framework that enables continuous and implicit user identification service for smartphone. Different from traditional active user authentication and access control, SenGuard leverages availability of multiple sensors on today's smartphones and passively use sensor inputs as sources of user authentication. It extracts sensor modality dependent user identification features from captured sensor data and performs user identification at background. SenGuard invokes active user authentication when there is a mounting evidence that the phone user has changed. In addition, SenGuard uses a novel virtualization based system architecture as a safeguard to prevent subversion of the background user identification mechanism by moving it into a privileged virtual domain. An initial prototype of SenGuard was created using four sensor modalities including, voice, location, multitouch, and locomotion. Preliminary empirical studies with a set of users indicate that those four modalities are suited as data sources for implicit mobile user identification. In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors — front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research. User identification is an essential problem for security protection and data privacy preservation of wearable devices. With proper user identification, wearable devices can adopt personalized settings for different users, automatically label the corresponding data to protect user privacy, and help prevent illegal user spoofing attacks. Current user identification solutions proposed for wearable devices either rely on dedicated devices with high cost or require user intervention which is not convenient. In this work, we leverage the bio-vibrometry to enable a novel user identification solution for wearable devices in small-scale scenarios, e.g., household scenario. Unlike existing user identification solutions, our system only uses the low-cost sensors that are already available for most wearable devices. The key idea is that, when human body is exposed to a vibration excitation, the vibration response reflects the physical characteristics of user, i.e., the mass, stiffness and damping. Meanwhile, due to users' biological diversity, such physical characteristics of different users are quite distinctive. Therefore, we can leverage the discrepancy in users' vibration responses as an identifier. Based on this idea, we propose VibID, which only uses a low-cost vibration motor and accelerometer to generate an unobtrusive vibration to users' arms and capture the corresponding responses. By examining the vibration patterns at different frequencies, VibID builds an ensemble machine learning model to recognize who is using the device. Extensive experiments are conducted on human subjects to demonstrate that our system is reliable in small- scale scenarios and robust to various confounding factors, e.g., arm position, muscle state, user mobility and wearing location. We also show that, in an uncontrolled scenario of 8 users, our system can still ensure a identification accuracy above 91 . Current smartphones generally cannot continuously authenticate users during runtime. This poses severe security and privacy threats: A malicious user can manipulate the phone if bypassing the screen lock. To solve this problem, our work adopts a continuous and passive authentication mechanism based on a user’s touch operations on the touchscreen. Such a mechanism is suitable for smartphones, as it requires no extra hardware or intrusive user interface. We study how to model multiple types of touch data and perform continuous authentication accordingly. As a first attempt, we also investigate the fundamentals of touch operations as biometrics by justifying their distinctiveness and permanence. A onemonth experiment is conducted involving over 30 users. Our experiment results verify that touch biometrics can serve as a promising method for continuous and passive authentication.