Phishing is recognized as a serious threat to organizations and individuals. While there have been significant technical advances in blocking phishing attacks, end-users remain the last line of defence after phishing emails reach their email inboxes. Most of the existing literature on this subject has focused on the technical aspects related to phishing. The factors that cause humans to be susceptible to phishing attacks are still not well-understood. To fill this gap, we reviewed the available literature and systematically categorized the phishing susceptibility variables studied. We classify variables based on their temporal scope, which led us to propose a three-stage Phishing Susceptibility Model (PSM) for explaining how humans are vulnerable to phishing attacks. This model reveals several research gaps that need to be addressed to understand and improve protection against phishing susceptibility. Our review also systematizes existing studies by their sample size and generalizability and further suggests a practical impact assessment of the value of studying variables: Some more easily lead to improvements than others. We believe that this article can provide guidelines for future phishing susceptibility research to improve experiment design and the quality of findings.

By developing awareness of smartphone activities that the user is performing on their smartphone, such as scrolling feeds, typing and watching videos, we can develop application features that are beneficial to the users, such as personalization. It is currently not possible to access real-time smartphone activities directly, due to standard smartphone privileges and if internal movement sensors can detect them, there may be implications for access policies. Our research seeks to understand whether the sensor data from existing smartphone inertial measurement unit (IMU) sensors (triaxial accelerometers, gyroscopes and magnetometers) can be used to classify typical human smartphone activities. We designed and conducted a study with human participants which uses an Android app to collect motion data during scrolling, typing and watching videos, while walking or seated and the baseline of smartphone non-use, while sitting and walking. We then trained a machine learning (ML) model to perform real-time activity recognition of those eight states. We investigated various algorithms and parameters for the best accuracy. Our optimal solution achieved an accuracy of 78.6% with the Extremely Randomized Trees algorithm, data sampled at 50 Hz and 5-s windows. We conclude by discussing the viability of using IMU sensors to recognize common smartphone activities.

Phishing is one of the most prevalent social engineering attacks that targets both organisations and individuals. It is crucial to understand how email presentation impacts users’ reactions to phishing attacks. We hypothesised that device type and email presentation could potentially play a role, particularly in how links are displayed, which might influence susceptibility. In collaboration with the IT Services unit of a large organisation for a phishing training exercise, we conducted a study to explore the effects of device type and link presentation. Our findings revealed no significant difference in users’ susceptibility to phishing when using mobile devices versus computers. However, the masking of phishing links as buttons or hypertext appeared to be influential in shaping users’ behaviour. More specifically, users were significantly more likely to click on phishing links when masked as hypertext. These findings suggest that link presentation plays a significant role in users’ susceptibility to phishing attacks.