Passwords have been a recurring subject of research ever since Morris and Thompson first pointed out their disadvantages in 1979. Several decades later, textual passwords remain to be the most used authentication method, despite the growing number of security breaches. In this article, we highlight technological advances that have the potential to ease brute-force attacks on longer passwords. We point out users? persistently bad password creation and management practices, arguing that the users will be unable to keep up with the increasingly demanding security requirements in the future. We examine a set of real, user-generated passwords, and compare them to the passwords collected by Morris and Thompson. The results show that today?s passwords remain as weak as they were nearly four decades ago. We provide insight on how the current password security could be improved by giving recommendations to users, administrators, and researchers. We dispute the reiterated claim that passwords should be replaced, by exposing the alternatives? weaknesses. Finally, we argue passwords will remain widespread until two conditions are met: First, a Pareto-improving authentication method is discovered, and second, the users are motivated to replace textual passwords.

Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this study, we investigate which function most appropriately describes learning curves produced by several machine learning algorithms, and how well these curves can predict the future performance of an algorithm. Decision trees, neural networks, Naïve Bayes, and Support Vector Machines were applied to 130 datasets from publicly available repositories. Three different functions (power, logarithmic, and exponential) were fit to the measured outputs. Using rigorous statistical methods and two measures for the goodness-of-fit, the power law model proved to be the most appropriate model for describing the learning curve produced by the algorithms in terms of goodness-of-fit and prediction capabilities. The presented study, first of its kind in scale and rigour, provides results (and methods) that can be used to assess the performance of novel or existing artificial learners and forecast their ‘capacity to learn’ based on the amount of available or desired data.

Despite being the most widely used method of authentication, passwords still pose a significant threat to an information system's security. This threat is mostly attributed to the human factor, as users tend to select passwords that are easy to remember, but are not resilient to brute force or dictionary attacks. Worse yet, when not prompted to change their passwords on a regular basis the users tend to keep their original passwords, or even default passwords set by the system. These bad practices have been addressed over the decades, with the intention to educate the users on the security risks associated with them. A case study on passwords, used by the students at a Slovenian university to access the online grading system was conducted to examine whether passwords have improved over the course of the years. The results have shown that the vast majority of students continue to use the generated default passwords. Of the rest of the students who have changed their passwords, a large percent use short, simple passwords, consisting mainly of alphabetic or numeric characters. With no specific password policies enforced, user-created passwords remain weak, showing that users are still the Achilles' heel of information security.