Eliciting Security Requirements and Tracing them to Design: An Integration of Common Criteria, Heuristics, and UMLsec

Building secure systems is difficult for many reasons. This paper deals with two of the main challenges: (i) the lack of security expertise in development teams, and (ii) the inadequacy of existing methodologies to support developers who are not security experts. The security standard ISO 14508 (Common Criteria) together with secure design techniques such as UMLsec can provide the security expertise, knowledge, and guidelines that are needed. However, security expertise and guidelines are not stated explicitly in the Common Criteria. They are rather phrased in security domain terminology and difficult to understand for developers. This means that some general security and secure design expertise are required to fully take advantage of the Common Criteria and UMLsec. In addition, there is the problem of tracing security requirements and objectives into solution design,which is needed for proof of requirements fulfilment. This paper describes a security requirements engineering methodology called SecReq. SecReq combines three techniques: the Common Criteria, the heuristic requirements editorHeRA, andUMLsec. SecReqmakes systematic use of the security engineering knowledge contained in the Common Criteria and UMLsec, as well as security-related heuristics in the HeRA tool. The integrated SecReq method supports early detection of security-related issues (HeRA), their systematic refinement guided by the Common Criteria, and the ability to trace security requirements into UML design models. A feedback loop helps reusing experiencewithin SecReq and turns the approach into an iterative process for the secure system life-cycle, also in the presence of system evolution.

A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

This paper outlines the development of a crosscorrelation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation.

Climbing Up the Leaderboard: An Empirical Study of Applying Gamification Techniques to a Computer Programming Class

Conventional taught learning practices often experience difficulties in keeping students motivated and engaged. Video games, however, are very successful at sustaining high levels of motivation and engagement through a set of tasks for hours without apparent loss of focus. In addition, gamers solve complex problems within a gaming environment without feeling fatigue or frustration, as they would typically do with a comparable learning task. Based on this notion, the academic community is keen on exploring methods that can deliver deep learner engagement and has shown increased interest in adopting gamification – the integration of gaming elements, mechanics, and frameworks into non-game situations and scenarios – as a means to increase student engagement and improve information retention. Its effectiveness when applied to education has been debatable though, as attempts have generally been restricted to one-dimensional approaches such as transposing a trivial reward system onto existing teaching materials and/or assessments. Nevertheless, a gamified, multi-dimensional, problem-based learning approach can yield improved results even when applied to a very complex and traditionally dry task like the teaching of computer programming, as shown in this paper. The presented quasi-experimental study used a combination of instructor feedback, real time sequence of scored quizzes, and live coding to deliver a fully interactive learning experience. More specifically, the “Kahoot!” Classroom Response System (CRS), the classroom version of the TV game show “Who Wants To Be A Millionaire?”, and Codecademy’s interactive platform formed the basis for a learning model which was applied to an entry-level Python programming course. Students were thus allowed to experience multiple interlocking methods similar to those commonly found in a top quality game experience. To assess gamification’s impact on learning, empirical data from the gamified group were compared to those from a control group who was taught through a traditional learning approach, similar to the one which had been used during previous cohorts. Despite this being a relatively small-scale study, the results and findings for a number of key metrics, including attendance, downloading of course material, and final grades, were encouraging and proved that the gamified approach was motivating and enriching for both students and instructors.