Högskolan i Skövde

Purpose Using authentication to secure data and accounts has grown to be a natural part of computing. Even if several authentication methods are in existence, using passwords remains the most common type of authentication. As long and complex passwords are encouraged by research studies and practitioners alike, computer users design passwords using strategies that enable them to remember their passwords. This paper aims to present a taxonomy of those password creation strategies in the form of a model describing various strategies used to create passwords. Design/methodology/approach The study was conducted in a three-step process beginning with a short survey among forensic experts within the Swedish police. The model was then developed by a series of iterative semi-structured interviews with forensic experts. In the third and final step, the model was validated on 5,000 passwords gathered from 50 different password databases that have leaked to the internet. Findings The result of this study is a taxonomy of password creation strategies presented as a model that describes the strategies as properties that a password can hold. Any given password can be classified as holding one or more of the properties outlined in the model. Originality/value On an abstract level, this study provides insight into password creation strategies. As such, the model can be used as a tool for research and education. It can also be used by practitioners in, for instance, penetration testing to map the most used password creation strategies in a domain or by forensic experts when designing dictionary attacks.

The purpose of this research was to answer the following central questions: 1) How accurate are human observers at predicting the behavior of cyclists as the cyclists approached a crossing? 2) If the accuracy is reliably better than chance, what cues were used to make the predictions? 3) At what distance from the crossing did the most critical cues occur? 4) Can the cues be used in a model that can reliably predict cyclist intent? We present results that show a number of indicators that can be used in to predict the intention of a cyclist, i.e., future actions of a cyclist, e.g., “left turn” or “continue forward” etc.

Results of empirical studies show that humans are reasonably good at this type of prediction for a majority of the situations studied. However, some situations seem to contain conflicting information. The results also suggested that human prediction of intention is to a large extent relying on a single “strong” indicator, e.g., that the cyclist makes a clear “head movement”. Several “weaker" indicators that together could be a strong “combined indicator”, or equivalently strong evidence, is likely to be missed or too complex to be handled by humans in real-time. We suggest this line of research can be used to create decision support systems that predict the behavior of cyclists in traffic.

Genom att titta på ett fåtal bestämda signaler kan man med god träffsäkerhet förutsäga cyklisters beteende, vilket tyder på att de identifierade signalerna är betydelsefulla. Vetskapen om dessa kan, bland annat, praktiskt användas för att utveckla enkla hjälpmedel – såsom medveten placering av fluorescerande eller reflekterande material på leder och/eller införande av olikfärgade hjälmsidor. Dylika kan förväntas förstärka kommunikationen av viktiga signaler. Vetskapen kan även användas för att utbilda oerfarna bilförare. Båda fallen kan i förlängningen ge en säkrare trafikmiljö för oskyddade trafikanter.