arXiv subjects

Valdemar Švábenský

Publications and source records attributed to Valdemar Švábenský.

At least 37 records · Page 2Linked to original sources

arXiv2024

Research and Practice of Delivering Tabletop Exercises

Tabletop exercises are used to train personnel in the efficient mitigation and resolution of incidents. They are applied in practice to support the preparedness of organizations and to highlight inefficient processes. Since tabletop exercises train competencies required in the workplace, they have been introduced into computing courses at universities as an innovation, especially within cybersecurity curricula. To help computing educators adopt this innovative method, we survey academic publications that deal with tabletop exercises. From 140 papers we identified and examined, we selected 14 papers for a detailed review. The results show that the existing research deals predominantly with exercises that follow a linear format and exercises that do not systematically collect data about trainees' learning. Computing education researchers can investigate novel approaches to instruction and assessment in the context of tabletop exercises to maximize the impact of this teaching method. Due to the relatively low number of published papers, the potential for future research is immense. Our review provides researchers, tool developers, and educators with an orientation in the area, a synthesis of trends, and implications for further work.

cs.CY↗

arXiv2024

Comparison of Three Programming Error Measures for Explaining Variability in CS1 Grades

Programming courses can be challenging for first year university students, especially for those without prior coding experience. Students initially struggle with code syntax, but as more advanced topics are introduced across a semester, the difficulty in learning to program shifts to learning computational thinking (e.g., debugging strategies). This study examined the relationships between students' rate of programming errors and their grades on two exams. Using an online integrated development environment, data were collected from 280 students in a Java programming course. The course had two parts. The first focused on introductory procedural programming and culminated with exam 1, while the second part covered more complex topics and object-oriented programming and ended with exam 2. To measure students' programming abilities, 51095 code snapshots were collected from students while they completed assignments that were autograded based on unit tests. Compiler and runtime errors were extracted from the snapshots, and three measures -- Error Count, Error Quotient and Repeated Error Density -- were explored to identify the best measure explaining variability in exam grades. Models utilizing Error Quotient outperformed the models using the other two measures, in terms of the explained variability in grades and Bayesian Information Criterion. Compiler errors were significant predictors of exam 1 grades but not exam 2 grades; only runtime errors significantly predicted exam 2 grades. The findings indicate that leveraging Error Quotient with multiple error types (compiler and runtime) may be a better measure of students' introductory programming abilities, though still not explaining most of the observed variability.

cs.PL↗

arXiv2023

Want to Raise Cybersecurity Awareness? Start with Future IT Professionals

As cyber threats endanger everyone, from regular users to computing professionals, spreading cybersecurity awareness becomes increasingly critical. Therefore, our university designed an innovative cybersecurity awareness course that is freely available online for students, employees, and the general public. The course offers simple, actionable steps that anyone can use to implement defensive countermeasures. Compared to other resources, the course not only suggests learners what to do, but explains why and how to do it. To measure the course impact, we administered it to 138 computer science undergraduates within a compulsory information security and cryptography course. They completed the course as a part of their homework and filled out a questionnaire after each lesson. Analysis of the questionnaire responses revealed that the students valued the course highly. They reported new learning, perspective changes, and transfer to practice. Moreover, they suggested suitable improvements to the course. Based on the results, we have distilled specific insights to help security educators design similar courses. Lessons learned from this study are relevant for cybersecurity instructors, course designers, and educational managers.

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arXiv2023

Towards Generalizable Detection of Urgency of Discussion Forum Posts

Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and students may lose the motivation to continue in the course. To help address this problem, we build predictive models that automatically determine the urgency of each forum post, so that these posts can be brought to instructors' attention. This paper goes beyond previous work by predicting not just a binary decision cut-off but a post's level of urgency on a 7-point scale. First, we train and cross-validate several models on an original data set of 3,503 posts from MOOCs at University of Pennsylvania. Second, to determine the generalizability of our models, we test their performance on a separate, previously published data set of 29,604 posts from MOOCs at Stanford University. While the previous work on post urgency used only one data set, we evaluated the prediction across different data sets and courses. The best-performing model was a support vector regressor trained on the Universal Sentence Encoder embeddings of the posts, achieving an RMSE of 1.1 on the training set and 1.4 on the test set. Understanding the urgency of forum posts enables instructors to focus their time more effectively and, as a result, better support student learning.

cs.LG↗

arXiv2023

Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering

Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees' interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees' learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.

cs.CR↗

arXiv2023

Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training

Cybersecurity professionals need hands-on training to prepare for managing the current advanced cyber threats. To practice cybersecurity skills, training participants use numerous software tools in computer-supported interactive learning environments to perform offensive or defensive actions. The interaction involves typing commands, communicating over the network, and engaging with the training environment. The training artifacts (data resulting from this interaction) can be highly beneficial in educational research. For example, in cybersecurity education, they provide insights into the trainees' learning processes and support effective learning interventions. However, this research area is not yet well-understood. Therefore, this paper surveys publications that enhance cybersecurity education by leveraging trainee-generated data from interactive learning environments. We identified and examined 3021 papers, ultimately selecting 35 articles for a detailed review. First, we investigated which data are employed in which areas of cybersecurity training, how, and why. Second, we examined the applications and impact of research in this area, and third, we explored the community of researchers. Our contribution is a systematic literature review of relevant papers and their categorization according to the collected data, analysis methods, and application contexts. These results provide researchers, developers, and educators with an original perspective on this emerging topic. To motivate further research, we identify trends and gaps, propose ideas for future work, and present practical recommendations. Overall, this paper provides in-depth insight into the recently growing research on collecting and analyzing data from hands-on training in security contexts.

cs.CY↗

arXiv2023

Smart Environment for Adaptive Learning of Cybersecurity Skills

Hands-on computing education requires a realistic learning environment that enables students to gain and deepen their skills. Available learning environments, including virtual and physical labs, provide students with real-world computer systems but rarely adapt the learning environment to individual students of various proficiency and background. We designed a unique and novel smart environment for adaptive training of cybersecurity skills. The environment collects a variety of student data to assign a suitable learning path through the training. To enable such adaptiveness, we proposed, developed, and deployed a new tutor model and a training format. We evaluated the learning environment using two different adaptive trainings attended by 114 students of various proficiency. The results show students were assigned tasks with a more appropriate difficulty, which enabled them to successfully complete the training. Students reported that they enjoyed the training, felt the training difficulty was appropriately designed, and would attend more training sessions like these. Instructors can use the environment for teaching any topic involving real-world computer networks and systems because it is not tailored to particular training. We freely released the software along with exemplary training so that other instructors can adopt the innovations in their teaching practice.

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arXiv2022

Scalable Learning Environments for Teaching Cybersecurity Hands-on

This Innovative Practice full paper describes a technical innovation for scalable teaching of cybersecurity hands-on classes using interactive learning environments. Hands-on experience significantly improves the practical skills of learners. However, the preparation and delivery of hands-on classes usually do not scale. Teaching even small groups of students requires a substantial effort to prepare the class environment and practical assignments. Further issues are associated with teaching large classes, providing feedback, and analyzing learning gains. We present our research effort and practical experience in designing and using learning environments that scale up hands-on cybersecurity classes. The environments support virtual networks with full-fledged operating systems and devices that emulate real-world systems. (...) Using the presented environments KYPO Cyber Range Platform and Cyber Sandbox Creator, we delivered the classes on-site or remotely for various target groups of learners (K-12, university students, and professional learners). The learners value the realistic nature of the environments that enable exercising theoretical concepts and tools. The instructors value time-efficiency when preparing and deploying the hands-on activities. Engineering and computing educators can freely use our software, which we have released under an open-source license. We also provide detailed documentation and exemplary hands-on training to help other educators adopt our teaching innovations and enable sharing of reusable components within the community.

cs.CY↗

arXiv2022

Reinforcing Cybersecurity Hands-on Training With Adaptive Learning

This paper presents how learning experience influences students' capability to learn and their motivation for learning. Although each student is different, standard instruction methods do not adapt to individuals. Adaptive learning reverses this practice and attempts to improve the student experience. While adaptive learning is well-established in programming, it is rarely used in cybersecurity education. This paper is one of the first works investigating adaptive learning in security training. First, we analyze the performance of 95 students in 12 training sessions to understand the limitations of the current training practice. Less than half of the students completed the training without displaying a solution, and only in two sessions, all students completed all phases. Then, we simulate how students would proceed in one of the past training sessions if it would offer more paths of various difficulty. Based on this simulation, we propose a novel tutor model for adaptive training, which considers students' proficiency before and during an ongoing training session. The proficiency is assessed using a pre-training questionnaire and various in-training metrics. Finally, we conduct a study with 24 students and new training using the proposed tutor model and adaptive training format. The results show that the adaptive training does not overwhelm students as the original static training. Adaptive training enables students to enter several alternative training phases with lower difficulty than the original training. The proposed format is not restricted to a particular training. Therefore, it can be applied to practicing any security topic or even in related fields, such as networking or operating systems. Our study indicates that adaptive learning is a promising approach for improving the student experience in security education. We also highlight implications for educational practice.

cs.CR↗

arXiv2022

Preventing Cheating in Hands-on Lab Assignments

Networking, operating systems, and cybersecurity skills are exercised best in an authentic environment. Students work with real systems and tools in a lab environment and complete assigned tasks. Since all students typically receive the same assignment, they can consult their approach and progress with an instructor, a tutoring system, or their peers. They may also search for information on the Internet. Having the same assignment for all students in class is standard practice efficient for learning and developing skills. However, it is prone to cheating when used in a summative assessment such as graded homework, a mid-term test, or a final exam. Students can easily share and submit correct answers without completing the assignment. In this paper, we discuss methods for automatic problem generation for hands-on tasks completed in a computer lab environment. Using this approach, each student receives personalized tasks. We developed software for generating and submitting these personalized tasks and conducted a case study. The software was used for creating and grading a homework assignment in an introductory security course enrolled by 207 students. The software revealed seven cases of suspicious submissions, which may constitute cheating. In addition, students and instructors welcomed the personalized assignments. Instructors commented that this approach scales well for large classes. Students rarely encountered issues while running their personalized lab environment. Finally, we have released the open-source software to enable other educators to use it in their courses and learning environments.

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arXiv2021

Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training

When learning cybersecurity, operating systems, or networking, students perform practical tasks using a broad range of command-line tools. Collecting and analyzing data about the command usage can reveal valuable insights into how students progress and where they make mistakes. However, few learning environments support recording and inspecting command-line inputs, and setting up an efficient infrastructure for this purpose is challenging. To aid engineering and computing educators, we share the design and implementation of an open-source toolset for logging commands that students execute on Linux machines. Compared to basic solutions, such as shell history files, the toolset's added value is threefold. 1) Its configuration is automated so that it can be easily used in classes on different topics. 2) It collects metadata about the command execution, such as a timestamp, hostname, and IP address. 3) Data are instantly forwarded to central storage in a unified, semi-structured format. This enables automated processing, both in real-time and post hoc, to enhance the instructors' understanding of student actions. The toolset works independently of the teaching content, the training network's topology, or the number of students working in parallel. We demonstrated the toolset's value in two learning environments at four training sessions. Over two semesters, 50 students played educational cybersecurity games using a Linux command-line interface. Each training session lasted approximately two hours, during which we recorded 4439 shell commands. The semi-automated data analysis revealed solution patterns, used tools, and misconceptions of students. Our insights from creating the toolset and applying it in teaching practice are relevant for instructors, researchers, and developers of learning environments. We provide the software and data resulting from this work so that others can use them.

cs.CR↗

arXiv2021

Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises

Cybersecurity students need to develop practical skills such as using command-line tools. Hands-on exercises are the most direct way to assess these skills, but assessing students' mastery is a challenging task for instructors. We aim to alleviate this issue by modeling and visualizing student progress automatically throughout the exercise. The progress is summarized by graph models based on the shell commands students typed to achieve discrete tasks within the exercise. We implemented two types of models and compared them using data from 46 students at two universities. To evaluate our models, we surveyed 22 experienced computing instructors and qualitatively analyzed their responses. The majority of instructors interpreted the graph models effectively and identified strengths, weaknesses, and assessment use cases for each model. Based on the evaluation, we provide recommendations to instructors and explain how our graph models innovate teaching and promote further research. The impact of this paper is threefold. First, it demonstrates how multiple institutions can collaborate to share approaches to modeling student progress in hands-on exercises. Second, our modeling techniques generalize to data from different environments to support student assessment, even outside the cybersecurity domain. Third, we share the acquired data and open-source software so that others can use the models in their classes or research.

cs.CY↗

arXiv2021

Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges

Capture the Flag challenges are a popular form of cybersecurity education, where students solve hands-on tasks in an informal, game-like setting. The tasks feature diverse assignments, such as exploiting websites, cracking passwords, and breaching unsecured networks. However, it is unclear how the skills practiced by these challenges match formal cybersecurity curricula defined by security experts. We explain the significance of Capture the Flag challenges in cybersecurity training and analyze their 15,963 textual solutions collected since 2012. Based on keywords in the solutions, we map them to well-established ACM/IEEE curricular guidelines to understand which skills the challenges teach. We study the distribution of cybersecurity topics, their variance in different challenge formats, and their development over the past years. The analysis showed the prominence of technical knowledge about cryptography and network security, but human aspects, such as social engineering and cybersecurity awareness, are neglected. We discuss the implications of these results and relate them to contemporary literature. Our results indicate that future Capture the Flag challenges should include non-technical aspects to address the current advanced cyber threats and attract a broader audience to cybersecurity.

cs.CR↗

arXiv2021

How Does a Student-Centered Course on Communication and Professional Skills Impact Students in the Long Run?

This Full Paper in the Research-To-Practice Category presents a long-term study about the effects of a student-centered course on communication and professional skills on students' thoughts, attitudes, and behavior. The course is offered at a European university as part of a computer science master's program. This paper shares the design and challenges of a longitudinal study that reaches ten years behind and employs a mixed-methods approach. Besides presenting and interpreting the findings, we shed light on which features tend to stay on students' minds and impact their way of being and acting in society. Moreover, we suggest implications for the design and practice in comparable courses to maximize constructive, sustainable effects, such as improved active listening, presentation skills, and openness to other perspectives. These are essential (not only) for computer science professionals. Our findings suggest that the course provided significant learning for the vast majority of respondents, including aspects such as presenting while keeping the other side in mind, managing one's stress, and becoming less shy to speak up. All in all, we aim to contribute an evidence-based source of motivation for instructors in technically focused curricula who hold a student-centered stance.

cs.CY↗

arXiv2020

Visual Feedback for Players of Multi-Level Capture the Flag Games: Field Usability Study

Capture the Flag games represent a popular method of cybersecurity training. Providing meaningful insight into the training progress is essential for increasing learning impact and supporting participants' motivation, especially in advanced hands-on courses. In this paper, we investigate how to provide valuable post-game feedback to players of serious cybersecurity games through interactive visualizations. In collaboration with domain experts, we formulated user requirements that cover three cognitive perspectives: gameplay overview, person-centric view, and comparative feedback. Based on these requirements, we designed two interactive visualizations that provide complementary views on game results. They combine a known clustering and time-based visual approaches to show game results in a way that is easy to decode for players. The purposefulness of our visual feedback was evaluated in a usability field study with attendees of the Summer School in Cyber Security. The evaluation confirmed the adequacy of the two visualizations for instant post-game feedback. Despite our initial expectations, there was no strong preference for neither of the visualizations in solving different tasks.

cs.HC↗

arXiv2020

Benefits and Pitfalls of Using Capture the Flag Games in University Courses

The concept of Capture the Flag (CTF) games for practicing cybersecurity skills is widespread in informal educational settings and leisure-time competitions. However, it is not much used in university courses. This paper summarizes our experience from using jeopardy CTF games as homework assignments in an introductory undergraduate course. Our analysis of data describing students' in-game actions and course performance revealed four aspects that should be addressed in the design of CTF tasks: scoring, scaffolding, plagiarism, and learning analytics capabilities of the used CTF platform. The paper addresses these aspects by sharing our recommendations. We believe that these recommendations are useful for cybersecurity instructors who consider using CTF games for assessment in university courses and developers of CTF game frameworks.

cs.CR↗

arXiv2020

KYPO4INDUSTRY: A Testbed for Teaching Cybersecurity of Industrial Control Systems

There are different requirements on cybersecurity of industrial control systems and information technology systems. This fact exacerbates the global issue of hiring cybersecurity employees with relevant skills. In this paper, we present KYPO4INDUSTRY training facility and a course syllabus for beginner and intermediate computer science students to learn cybersecurity in a simulated industrial environment. The training facility is built using open-source hardware and software and provides reconfigurable modules of industrial control systems. The course uses a flipped classroom format with hands-on projects: the students create educational games that replicate real cyber attacks. Throughout the semester, they learn to understand the risks and gain capabilities to respond to cyber attacks that target industrial control systems. Our described experience from the design of the testbed and its usage can help any educator interested in teaching cybersecurity of cyber-physical systems.

cs.CR↗

arXiv2020

Conceptual Model of Visual Analytics for Hands-on Cybersecurity Training

Hands-on training is an effective way to practice theoretical cybersecurity concepts and increase participants' skills. In this paper, we discuss the application of visual analytics principles to the design, execution, and evaluation of training sessions. We propose a conceptual model employing visual analytics that supports the sensemaking activities of users involved in various phases of the training life cycle. The model emerged from our long-term experience in designing and organizing diverse hands-on cybersecurity training sessions. It provides a classification of visualizations and can be used as a framework for developing novel visualization tools supporting phases of the training life-cycle. We demonstrate the model application on examples covering two types of cybersecurity training programs.

cs.HC↗