Improving data quality for human-as-a-security-sensor. A process driven quality improvement approach for user-provided incident information

Abstract

Purpose In the past, people were usually seen as the weakest link in the IT security chain. However, this view has changed in recent years and people are no longer seen only as a problem, but also as part of the solution. In research, this change is reflected in the fact that people are enabled to report security incidents that they have detected. During this reporting process, however, it is important to ensure that the reports are submitted with the highest possible data quality. This paper aims to provide a process-driven quality improvement approach for human-as-a-security-sensor information. Design/methodology/approach This work builds upon existing approaches for structured reporting of security incidents. In the first step, relevant data quality dimensions and influencing factors are defined. Based on this, an approach for quality improvement is proposed. To demonstrate the feasibility of the approach, it is prototypically implemented and evaluated using an exemplary use case. Findings In this paper, a process-driven approach is proposed, which allows improving the data quality by analyzing the similarity of incidents. It is shown that this approach is feasible and leads to better data quality with real-world data. Originality/value The originality of the approach lies in the fact that data quality is already improved during the reporting of an incident. In addition, approaches from other areas, such as recommender systems, are applied innovatively to the area of the human-as-a-security-sensor.

Formalizing and Integrating User Knowledge into Security Analytics

“…For example, if an employee notices a security incident, they need to be able to contribute their observations to the Data Collection phase of the Incident Detection Lifecycle. Initial approaches to this already exist in the form of the human-as-a-security-sensor paradigm [32,33]. However, further research is needed in this direction to solve this problem in an applicable way.…”

A visual analytics approach for the assessment of information quality of performance models—a software review

“…Analyzing and steering Data Quality in analytical processes are very relevant activities in computer science and data analysis [e.g., Ahmed et al (2018) for improvement of data quality in intelligent e-CRM applications, or Vielberth et al (2021) for security incidents], where many automatic tools exist that support these tasks, be it to different degrees. Recent work by Ehrlinger et al (2019) surveyed 667 software tools dedicated to data quality.…”

“…The study confirmed that showing data quality measures and allowing the user to consider them during data exploration produces different behavior than not doing it, and it is mentioned as further motivation for our proposal. Vielberth et al (2021) propose a process-driven quality improvement approach for human-as-a-security-sensor information. However, differently from our approach, the authors just provide the process and a use case based on tabular data, without providing any means, visual or not, to support the user in this process.…”