Smartphones are ubiquitously integrated into our home and work environment and users frequently use them as the portal to\ncloud-based secure services. Since smartphones can easily be stolen or coopted, the advent of smartwatches provides an intriguing\nplatform legitimate user identification for applications like online banking and many other cloud-based services. However, to\naccess security-critical online services, it is highly desirable to accurately identifying the legitimate user accessing such services and\ndata whether coming from the cloud or any other source. Such identification must be done in an automatic and non-bypassable\nway. For such applications, this work proposes a two-fold feasibility study; (1) activity recognition and (2) gait-based legitimate\nuser identification based on individual activity. To achieve the above-said goals, the first aim of this work was to propose\na semicontrolled environment system which overcomes the limitations of usersâ?? age, gender, and smartwatch wearing style. The\nsecond aim of this work was to investigate the ambulatory activity performed by any user. Thus, this paper proposes a novel system\nfor implicit and continuous legitimate user identification based on their behavioral characteristics by leveraging the sensors\nalready ubiquitously built into smartwatches. The design system gives legitimate user identification using machine learning\ntechniques and multiple sensory data with 98.68% accuracy.
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