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Security risk analysis (SRA) is a key activity in software engineering but requires heavy manual effort. Community knowledge in the form of security patterns or security catalogs can be used to support the identification of threats and security controls. However, no evidence-based theory exists about the effectiveness of security catalogs when used for security risk analysis. We adopt a grounded theory approach to propose a conceptual, revised and refined theory of SRA knowledge reuse. The theory refinement is backed by evidence gathered from conducting interviews with experts (20) and controlled experiments with both experts (15) and novice analysts (18). We conclude the paper by providing insights into the use of catalogs and managerial implications.

Software updates reduce the opportunity for exploitation. However, since updates can also introduce breaking changes, enterprises face the problem of balancing the need to secure software with updates with the need to support operations. We propose a methodology to quantitatively investigate the effectiveness of software updates strategies against attacks of Advanced Persistent Threats (APTs). We consider strategies where the vendor updates are the only limiting factors to cases in which enterprises delay updates from 1 to 7 months based on SANS data. Our manually curated dataset of APT attacks covers 86 APTs and 350 campaigns from 2008 to 2020. It includes information about attack vectors, exploited vulnerabilities (e.g., 0-days versus public vulnerabilities), and affected software and versions. Contrary to common belief, most APT campaigns employed publicly known vulnerabilities. If an enterprise could theoretically update as soon as an update is released, it would face lower odds of being compromised than those waiting one (4.9x) or three (9.1x) months. However, if attacked, it could still be compromised from 14% to 33% of the times. As in practice enterprises must do regression testing before applying an update, our major finding is that one could perform 12% of all possible updates restricting oneself only to versions fixing publicly known vulnerabilities without significant changes to the odds of being compromised compared to a company that updates for all versions.

To kick-start the discussion, let's first review some of the recent attacks. In the node-ipc case1 a developer pushed an update that deliberately but stealthily included code that sabotaged the computer of the users who installed the updated component. Such an attack was selective: a DarkSide in reverse. If the computer Internet Protocol (IP) was geolocated in Russia, the attack would be launched. Several days and a few million downloads later, the spurious code was actually noticed and investigated. Linus's law on the many eyes eventually made the bug shallow,2 and the developer pulled back the changes.

This dataset contains expert assessments of the cybersecurity skills required for six job profiles in Europe, as determined via surveys responded by cybersecurity experts from academia and industry. The data can be used to identify educational needs in the cybersecurity sector and compare against other frameworks. The six cybersecurity-oriented job profiles used in the surveys are: General cybersec auditor; Technical cybersec auditor; Threat modelling engineer; Security engineer; Enterprise cybersecurity practitioner; Cybersecurity analyst. Data—i.e. expert assessments—was collected via surveys, targeted at European experts in cybersecurity from academia and industry. Respondents characterised the skills needed to perform in six job profiles using the CSEC+ framework: a cybersecurity skills framework prepared as a spreadsheet where cybersecurity skills must be ranked in a Likert scale from 0 (irrelevant) to 4 (advance knowledge needed). Metadata requested included the type of organisation of the respondent (Large company, SME, Academic/Research, Public administration, Other) and the country of origin. There were three data-collection phases: (1) an initial phase, used also to refine later larger-scale processes, carried out in Oct 2021–Jan 2022 and resulting in 13 expert assessments from four EU countries; (2) a second phase implemented as an online service broadcast to a larger audience, carried out in Mar–Apr 2022 and resulting in 15 assessments from eight European countries; (3) and a third phase, allowing direct online input and distributed in PC and mobile form, carried out in Sep–Oct 2022 and resulting in 32 assessments from ten European countries. The raw data gathered was stored and processed via spreadsheets, computing statistical information (mean, stdev) on how much each cybersecurity skill and area was deemed necessary to perform in each job profile. This is visualised as a heatmap where colour intensity symbolises value, and circle diffusion symbolises spread. Processed data further includes visualisations on how the area of origin of the respondent (academia, as in “producer of education”, vs. industry, as in “consumer of education”) influences the responses. This is shown as bar plots, where whiskers represent confidence intervals used for statistical-significance tests. This data can serve as basis to understand the educational needs for the cybersecurity sector in Europe. It can be reused for comparison against frameworks, other than CSEC+, to assess the need of education in specific cybersecurity sectors such as human security. Furthermore, the Qualtrics survey template (included) is a ready-made solution for replication studies.