Enhancing Security Requirements Engineering by Organisational Learning

More and more software projects today are security-related in one way or the other. Requirements engineers often fail to recognise indicators for security problems which is a major source of security problems in practice. Identifying security-relevant requirements is labour-intensive and errorprone. In order to facilitate the security requirements elicitation process, we present an approach supporting organisational learning on security requirements by establishing company-wide experience resources, and a socio-technical network to benefit from them. The approach is based on modelling the flow of requirements and related experiences. Based on those models, we enable people to exchange experiences about security-requirements while they write and discuss project requirements. At the same time, the approach enables participating stakeholders to learn while they write requirements. This can increase security awareness and facilitate learning on both individual and organisational levels. As a basis for our approach, we introduce heuristic assistant tools which support reuse of existing security-related experiences. In particular, they include Bayesian classifiers which issue a warning automatically when new requirements seem to be security-relevant. Our results indicate that this is feasible, in particular if the classifier is trained with domain specific data and documents from previous projects. We show how the ability to identify security-relevant requirements can be improved using this approach. We illustrate our approach by providing a step-by-step example of how we improved the security requirements engineering process at the European Telecommunications Standards Institute (ETSI) and report on experiences made in this application.

Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

Effects of Prochlorperazine on Normal Vestibular Ocular and Perceptual Responses: A Randomised, Double-Blind, Crossover, Placebo-Controlled Study

Background: The present study investigated whether prochlorperazine affects vestibular-ocular reflex (VOR) and vestibulo-perceptual function. Methods: We studied 12 healthy naïve subjects 3 hours after a single dose of oral prochlorperazine 5mg in a randomised, placebo-controlled, double-blind, cross-over study in healthy young subjects. Two rotational tests in yaw were used: 1) a Threshold task investigating perceptual motion detection and nystagmic thresholds (acceleration steps of 0.5deg/s/s) and 2) Suprathreshold responses to velocity steps of 90deg/s in which vestibulo-ocular (VO) and vestibulo-perceptual (VP) time constants of decay, as well as VOR gain, were measured. Results: Prochlorperazine had no effect upon any measure of nystagmic or perceptual vestibular function compared to placebo. This lack of effects on vestibular-mediated motion perception suggests that the drug is likely to act more as an antiemetic than as an anti-vertiginous agent.