Biblio

The Security Behavior Intentions Scale (SeBIS) measures the computer security attitudes of end-users. Because intentions are a prerequisite for planned behavior, the scale could therefore be useful for predicting users' computer security behaviors. We performed three experiments to identify correlations between each of SeBIS's four sub-scales and relevant computer security behaviors. We found that testing high on the awareness sub-scale correlated with correctly identifying a phishing website; testing high on the passwords sub-scale correlated with creating passwords that could not be quickly cracked; testing high on the updating sub-scale correlated with applying software updates; and testing high on the securement sub-scale correlated with smartphone lock screen usage (e.g., PINs). Our results indicate that SeBIS predicts certain computer security behaviors and that it is a reliable and valid tool that should be used in future research.

Computer security problems often occur when there are disconnects between users’ understanding of their role in computer security and what is expected of them. To help users make good security decisions more easily, we need insights into the challenges they face in their daily computer usage. We built and deployed the Security Behavior Observatory (SBO) to collect data on user behavior and machine configurations from participants’ home computers. Combining SBO data with user interviews, this paper presents a qualitative study comparing users’ attitudes, behaviors, and understanding of computer security to the actual states of their computers. Qualitative inductive thematic analysis of the interviews produced “engagement” as the overarching theme, whereby participants with greater engagement in computer security and maintenance did not necessarily have more secure computer states. Thus, user engagement alone may not be predictive of computer security. We identify several other themes that inform future directions for better design and research into security interventions. Our findings emphasize the need for better understanding of how users’ computers get infected, so that we can more effectively design user-centered mitigations.