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Despite widespread use of smartphones, there is no measurement standard targeted at smartphone security behaviors. In this paper we translate a well-known cybersecurity behavioral scale into the smartphone domain and show that we can improve on this translation by following an established psychometrics approach surveying 1011 participants. We design a new 14-item Smartphone Security Behavioral Scale (SSBS) exhibiting high reliability and good fit to a two-component behavioural model based on technical versus social protection strategies. We then demonstrate how SSBS can be applied to measure the influence of mental health issues on smartphone security behavior intentions. We found significant correlations that predict SSBS profiles from three types of MHIs. Conversely, we are able to predict presence of MHIs using SSBS profiles.We obtain prediction AUCs of 72.1% for Internet addiction,75.8% for depression and 66.2% for insomnia.
The purpose of the covert communication system is to implement the communication process without causing third party perception. In order to achieve complete covert communication, two aspects of security issues need to be considered. The first one is
Continuous, ubiquitous monitoring through wearable sensors has the potential to collect useful information about users context. Heart rate is an important physiologic measure used in a wide variety of applications, such as fitness tracking and health
Android unlock patterns remain quite common. Our study, as well as others, finds that roughly 25% of respondents use a pattern when unlocking their phone. Despite known security issues, the design of the pattern interface remains unchanged since firs
Smartphones have been employed with biometric-based verification systems to provide security in highly sensitive applications. Audio-visual biometrics are getting popular due to the usability and also it will be challenging to spoof because of multi-
We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making. We pr