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Malicious attackers frequently breach information systems by exploiting disclosed software vulnerabilities. Knowledge of these vulnerabilities over time is essential to decide the use of software products by organisations. The purpose of this paper is to propose a novel G-RAM framework for business organisations to assess and mitigate risks arising out of software vulnerabilities.

The G-RAM risk assessment module uses GARCH to model vulnerability growth. Using 16-year data across 1999-2016 from the National Vulnerability Database, the authors estimate the model parameters and validate the prediction accuracy. Next, the G-RAM risk mitigation module designs optimal software portfolio using Markowitz’s mean-variance optimisation for a given IT budget and preference.

Based on an empirical analysis, this study establishes that vulnerability follows a non-linear, time-dependent, heteroskedastic growth pattern. Further, efficient software combinations are proposed that optimise correlated risk. The study also reports the empirical evidence of a shift in efficient frontier of software configurations with time.

Existing assumption of independent and identically distributed residuals after vulnerability function fitting is incorrect. This study applies GARCH technique to measure volatility clustering and mean reversal. The risk (or volatility) represented by the instantaneous variance is dependent on the immediately previous one, as well as on the unconditional variance of the entire vulnerability growth process.

The volatility-based estimation of vulnerability growth is a risk assessment mechanism. Next, the portfolio analysis acts as a risk mitigation activity. Results from this study can decide patch management cycle needed for each software – individual or group patching. G-RAM also ranks them into a 2×2 risk-return matrix to ensure that the correlated risk is diversified. Finally the paper helps the business firms to decide what to purchase and what to avoid.

Contrary to the existing techniques which either analyse with statistical distributions or linear econometric methods, this study establishes that vulnerability growth follows a non-linear, time-dependent, heteroskedastic pattern. The paper also links software risk assessment to IT governance and strategic business objectives. To the authors’ knowledge, this is the first study in IT security to examine and forecast volatility, and further design risk-optimal software portfolios.

In the last four years, since Volume I of this Bibliography first appeared, there has been an explosion of literature in all the main functional areas of business. This wealth of material poses problems for the researcher in management studies — and, of course, for the librarian: uncovering what has been written in any one area is not an easy task. This volume aims to help the librarian and the researcher overcome some of the immediate problems of identification of material. It is an annotated bibliography of management, drawing on the wide variety of literature produced by MCB University Press. Over the last four years, MCB University Press has produced an extensive range of books and serial publications covering most of the established and many of the developing areas of management. This volume, in conjunction with Volume I, provides a guide to all the material published so far.