Purpose Boards of Directors and other organisational leaders make decisions about the information security governance systems to implement in their companies. The increasing number of cyber-breaches targeting businesses makes this activity inescapable. Recently, researchers have published comprehensive lists of recommended cyber measures, specifically to inform organisational boards. However, the young cybersecurity industry has still to confirm and refine these guidelines. As a starting point, it would be helpful for organisational leaders to know what other organisations are doing in terms of using these guidelines. In an ideal world, bespoke surveys would be developed to gauge adherence to guidelines, but this is not always feasible. What we often do have is data from existing cybersecurity surveys. The authors argue that such data could be repurposed to quantify adherence to existing information security guidelines, and this paper aims to propose, and test, an original methodology to do so. Design/methodology/approach The authors propose a quantification mechanism to measure the degree of adherence to a set of published information security governance recommendations and guidelines targeted at organisational leaders. The authors test their quantification mechanism using a data set collected in a survey of 156 Italian companies on information security and privacy. Findings The evaluation of the proposed mechanism appears to align with findings in the literature, indicating the validity of the present approach. An analysis of how different industries rank in terms of their adherence to the selected set of recommendations and guidelines confirms the usability of our repurposed data set to measure adherence. Originality/value To the best of the authors’ knowledge, a quantification mechanism as the one proposed in this study has never been proposed, and tested, in the literature. It suggests a way to repurpose survey data to determine the extent to which companies are implementing measures recommended by published cybersecurity guidelines. This way, the proposed mechanism responds to increasing calls for the adoption of research practices that minimise waste of resources and enhance research sustainability.
The latest population estimates released by Statistics South Africa indicate that 25.03% of all deaths in 2017 in South Africa were AIDS-related. Along with these results, it is also reported that 7.06% of the population were living with HIV, with the HIV-prevalence among youth (aged 15–24) at 4.64% for 2017 (STATSSA. (2018). Retrieved from Statistics South Africa: http://www.statssa.gov.za/publications/P0302/P03022017.pdf ). The data used in the study contained information related to the risk-taking behaviours associated with the sexual activity of entering first-year students at the University of the Western Cape. In this study, a logistic regression modelling procedure was carried out on those students that were determined to be sexually active, therefore, in the modelling procedure significant risk behaviours of sexually active first-year students could be identified. Of the 14 variables included in the modelling procedure, six were found to be significantly associated with sexually active students. The significant variables included; the age and race of the student, whether the student had ever taken an HIV test, the importance of religion in influencing the sexual behaviour of the student, whether the student consumed alcohol and lastly whether the student smoked. This study further investigated the impact of introducing sample weighting, bootstrap sampling as well as variable selection methods into the logistic regression modelling procedure. It is shown that incorporating these techniques into the modelling procedure produces logistic regression models that are more accurate and have an increased predictive capability. The bootstrapping procedure is shown to produce logistic regression models that are more accurate than those produced without a bootstrap procedure. A comparison between 200, 500 and 1000 bootstrap samples is also incorporated into the modelling procedure with the models produced from 200 bootstrap samples shown to be just as accurate those produced from 500 or 1000 bootstrap samples. Of the five variable selection methods used, it is shown that the Newton–Raphson and Fisher methods are unreliable in producing logistic regression models. The forward, backward and stepwise variable selection methods are shown to produce very similar results.
Discretising (or binning) continuous predictors forms part of the data preparation and exploration phase when building predictive models. Discretisation involves partitioning a predictor variable's support into a set of disjunct intervals separated by cut-points. Discretisation is a valuable tool in predictive modelling and preliminary variable selection methods. It eases the interpretability and assists researchers in gauging the relationship between a predictor and the response of interest while providing automatic protection against outliers and missing values. Some machine learning and data mining techniques work better with categorical or discretized continuous variables instead of raw quantitative values. In some instances, the discretisation process must consider business, operational, or best practice constraints concerning selected intervals and parameter trends across consecutive intervals of a predictor variable. Choosing cut-points while maintaining a specified relationship trend between the discretised predictor and the response variable while adhering to additional side constraints, is difficult. The aim is to provide a concise summary of a predictor variable with as little loss of predictive power as possible while satisfying side constraints. This optimal trade-off between predictive power and arity for a constrained solution space allows the discretisation problem to be formulated as a mathematical optimisation problem. This paper provides a flexible mixed-integer programming formulation for the constrained optimal supervised discretisation problem with automatic trend detection using a novel network flow approach. The flexibility of the proposed framework allows us to implement and compare different supervised discretisation predictive performance measures on a real-world dataset.
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