Compete to Learn: Toward Cybersecurity as a Sport

To support the workforce gap of skilled cybersecurity professionals, gamified pedagogical approaches for teaching cybersecurity have exponentially grown over the last two decades. During this same period, e-sports developed into a multi-billion dollar industry and became a staple on college campuses. In this work, we explore the opportunity to integrate e-sports and gamified cybersecurity approaches into the inaugural US Cyber Games Team. During this tenure, we learned many lessons about recruiting, assessing, and training cybersecurity teams. We share our approach, materials, and lessons learned to serve as a model for fielding amateur cybersecurity teams for future competition

MAnanA: A Generalized Heuristic Scoring Approach for Concept Map Analysis as Applied to Cybersecurity Education

Concept Maps (CMs) are considered a well-known pedagogy technique in creating curriculum, educating, teaching, and learning. Determining comprehension of concepts result from comparisons of candidate CMs against a master CM, and evaluate goodness . Past techniques for comparing CMs have revolved around the creation of a subjective rubric. We propose a novel CM scoring scheme called MAnanA based on a Fuzzy Similarity Scaling (FSS) score to vastly remove the subjectivity of the rubrics in the process of grading a CM. We evaluate our framework against a predefined rubric and test it with CM data collected from the Introduction to Computer Security course at the University of New Orleans (UNO), and found that the scores obtained via MAnanA captured the trend that we observed from the rubric via peak matching. Based on our evaluation, we believe that our framework can be used to objectify CM analysis

Assessment Of Two Pedagogical Tools For Cybersecurity Education

Cybersecurity is an important strategic areas of computer science, and a difficult discipline to teach effectively. To enhance and provide effective teaching and meaningful learning, we develop and assess two pedagogical tools: Peer instruction, and Concept Maps. Peer instruction teaching methodology has shown promising results in core computer science courses by reducing failure rates and improving student retention in computer science major. Concept maps are well-known technique for improving student-learning experience in class. This thesis document presents the results of implementing and evaluating the peer instruction in a semester-long cybersecurity course, i.e., introduction to computer security. Development and evaluation of concept maps for two cybersecurity courses: SCADA security systems, and digital forensics. We assess the quality of the concept maps using two well-defined techniques: Waterloo rubric, and topological scoring. Results clearly shows that overall concept maps are of high-quality and there is significant improvement in student learning gain during group-discussion

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

The Threat of Offensive AI to Organizations

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this study, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary’s methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 32 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a panel survey spanning industry, government and academia, we rank the AI threats and provide insights on the adversaries

Development of Peer Instruction Material for a Cybersecurity Curriculum

Cybersecurity classes focus on building practical skills alongside the development of the open mindset that is essential to tackle the dynamic cybersecurity landscape. Unfortunately, traditional lecture-style teaching is insufficient for this task. Peer instruction is a non-traditional, active learning approach that has proven to be effective in computer science courses. The challenge in adopting peer instruction is the development of conceptual questions. This thesis presents a methodology for developing peer instruction questions for cybersecurity courses, consisting of four stages: concept identification, concept trigger, question presentation, and development. The thesis analyzes 279 questions developed over two years for three cybersecurity courses: introduction to computer security, network penetration testing, and introduction to computer forensics. Additionally, it discusses examples of peer instruction questions in terms of the methodology. Finally, it summarizes the usage of a workshop for testing a selection of peer instruction questions as well as gathering data outside of normal courses

Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission