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Cybersecurity training plays a decisive role in overcoming the global shortage of cybersecurity experts and the risks this shortage poses to organizations' assets. Seeking to make the training of those experts as efficacious and efficient as possible, this study investigates the potential of visual programming languages (VPLs) for training in cyber ranges. For this matter, the VPL Blockly was integrated into an existing cyber range training to facilitate learning a code-based cybersecurity task, namely, creating code-based correlation rules for a security information and event management (SIEM) system.

To evaluate the VPL’s effect on the cyber range training, the authors conducted a user study as a randomized controlled trial with 30 participants. In this study, the authors compared skill development of participants creating SIEM rules using Blockly (experimental group) with participants using a textual programming approach (control group) to create the rules.

This study indicates that using a VPL in a cybersecurity training can improve the participants' perceived learning experience compared to the control group while providing equally good learning outcomes.

The originality of this work lies in studying the effect of using a VPL to learn a code-based cybersecurity task. Investigating this effect in comparison with the conventional textual syntax through a randomized controlled trial has not been investigated yet.

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.

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.

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.

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.