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.
Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments
University of Pennsylvania（宾夕法尼亚大学）；Masaryk University（马萨里克大学）；Georgia Institute of Technology（佐治亚理工学院）；The Evergreen State College（常青州立学院）；Lewis & Clark College（刘易斯与克拉克学院）；Northern Kentucky University（北肯塔基大学）
Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection
Kennesaw State University（肯尼索州立大学）；Marquette University（马凯特大学）；Clark Atlanta University（克拉克亚特兰大大学）；Clayton State University（克莱顿州立大学）；Georgia Southern University（佐治亚南方大学）；Florida International University（佛罗里达国际大学）；Auburn University（奥本大学）；Tuskegee University（塔斯基吉大学）
A Non-Invasive Cloud-Based Migration Strategy for Post-Quantum Cybersecurity in Smart HVAC Systems: Architecture, Implementation, and Empirical Evaluation
Islamic University of Technology, Gazipur, Bangladesh（伊斯兰理工大学，加济布尔，孟加拉国）；Laurentian University, Sudbury, ON, Canada（劳伦森大学，萨德伯里，安大略省，加拿大）；Centennial College, Toronto, ON, Canada（百年理工学院，多伦多，安大略省，加拿大）
GuardSec: A Multi-Modal Web Platform for Real-Time Digital Fraud Detection, Entity Verification, and Connection Security Analysis in the African Context
Université Marien Ngouabi（马里安·恩古瓦比大学）；Société Informatique du Congo (SIC)（刚果计算机公司）
Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption
Sulaimani Polytechnic University（苏莱曼尼理工大学）；Charmo University（查尔莫大学）；Ministry of Education（教育部）；Hawazen Preparatory School for Girls（哈瓦森女子预备学校）；University of Kurdistan Hewler（埃尔比勒库尔德斯坦大学）
