Changing users' security behaviour towards security questions: A game based learning approach

Fallback authentication is used to retrieve forgotten passwords. Security questions are one of the main techniques used to conduct fallback authentication. In this paper, we propose a serious game design that uses system-generated security questions with the aim of improving the usability of fallback authentication. For this purpose, we adopted the popular picture-based "4 Pics 1 word" mobile game. This game was selected because of its use of pictures and cues, which previous psychology research found to be crucial to aid memorability. This game asks users to pick the word that relates to the given pictures. We then customized this game by adding features which help maximize the following memory retrieval skills: (a) verbal cues - by providing hints with verbal descriptions, (b) spatial cues - by maintaining the same order of pictures, (c) graphical cues - by showing 4 images for each challenge, (d) interactivity/engaging nature of the game.Comment: 6, Military Communications and Information Systems Conference (MilCIS), 2017. arXiv admin note: substantial text overlap with arXiv:1707.0807

A Model for Enhancing Human Behaviour with Security Questions: A Theoretical Perspective

Security questions are one of the mechanisms used to recover passwords. Strong answers to security questions (i.e. high entropy) are hard for attackers to guess or obtain using social engineering techniques (e.g. monitoring of social networking profiles), but at the same time are difficult to remember. Instead, weak answers to security questions (i.e. low entropy) are easy to remember, which makes them more vulnerable to cyber-attacks. Convenience leads users to use the same answers to security questions on multiple accounts, which exposes these accounts to numerous cyber-threats. Hence, current security questions implementations rarely achieve the required security and memorability requirements. This research study is the first step in the development of a model which investigates the determinants that influence users' behavioural intentions through motivation to select strong and memorable answers to security questions. This research also provides design recommendations for novel security questions mechanisms.Comment: 11, Australasian Conference on Information Systems, 201

A Serious Game Design: Nudging Users’ Memorability of Security Questions

Online review communities thrive on contributions from different reviewers, who exhibit a varying range of community behaviors. However, no attempt has been made in the IS literature to cluster behavioral patterns across a reviewer population. In this paper, we segment the reviewers of a popular review site (Yelp) using two-step cluster analysis based on four key attributes (reviewer involvement, sociability, experience, and review quality), resulting in three distinct reviewer segments - Enthusiasts, Adepts, and Amateurs. We also compare the propensity of receiving community recognition across these segments. We find that the Enthusiasts, who show high involvement and sociability, are the most recognized. Surprisingly, the Adepts, who are high on review quality, are the least recognized. The study is a novel attempt on reviewer segmentation and provides valuable insights to the community managers to customize strategies to increase productivity of different segments

A Model for Enhancing Human Behaviour with Security Questions: A Theoretical Perspective

In recent years, technological improvements have provided a variety of new opportunities for insurance companies to adopt telematics devices in line with usage-based insurance models. This paper sheds new light on the application of big data analytics for car insurance companies that may help to estimate the risks associated with individual policyholders based on complex driving patterns. We propose a conceptual framework that describes the structural design of a risk predictor model for insurance customers and combines the value of telematics data with deep learning algorithms. The model’s components consist of data transformation, criteria mining, risk modelling, driving style detection, and risk prediction. The expected outcome is our methodology that generates more accurate results than other methods in this area

The LHC magnetic field model

The compensation of the field changes during the beam injection and acceleration in the LHC requires an accurate forecast and an active control of the magnetic field in the accelerator. The LHC Magnetic Field Model is the core of this magnetic prediction system. The model will provide the desired field components at a given time, magnet operating current, magnet ramp rate, magnet temperature and magnet powering history to the required precision. The model is based on the identification and physical decomposition of the effects that contribute to the total field in the magnet aperture of the LHC dipoles. Each effect is quantified using data obtained from series measurements, and modeled theoretically or empirically depending on the complexity of the physical phenomena involved. This paper presents the developments of the new finely tuned magnetic field model and evaluates its accuracy and predictive capabilities over a sector of the machine.peer-reviewe