Use of Machine Learning in Interactive Cybersecurity and Network Education

Abstract

Cybersecurity is a complex subject for students to pursue. Hands-on online learning through labs and simulations can help students become more familiar with the subject at security classes to pursue cybersecurity education. There are several online tools and simulation platforms for cybersecurity education. However, those platforms need more constructive feedback mechanisms, and customizable hands-on exercises for users, or they oversimplify or misrepresent the content. In this paper, we aim to develop a platform for cybersecurity education that can be used either with a user interface or command line and provide auto constructive feedback for command line practices. Moreover, the platform currently has nine levels to practice for different subjects of networking and cybersecurity and a customizable level to create a customized network structure to test. The difficulty of objectives increases at each level. Moreover, an automatic feedback mechanism is developed by using a machine learning model to warn users about their typographical errors while using the command line to practice. A trial was performed with students completing a survey before and after using the application to test the effects of auto-feedback on users’ understanding of the subjects and engagement with the application. The machine learning-based version of the application has a net increase in the user ratings of almost every survey field, such as user-friendliness and overall experience.

Digital innovation through cybersecurity learning factories

“…Interactive training sessions, including educational games, are expected to have the highest learning effect among the applied learning methods because of providing opportunities for exploration, experimentation and problem solving (Ratan & Ritterfield 2009)Emerging technologies such as machine learning can be incorporated in gamebased learning factories to increase interactivity between user and platform, thus further improving the quality of learning and teaching. In a cybersecurity learning factory, machine learning technology can be used to provide real-time and customised feedback to the user by detecting wrong inputs such as a syntax error in a programming language, interpreting that, and providing the most likely set of inputs a user may choose from to correct their input (Loftus & Narman, 2023). Personalised feedback has been proven to enhance learning, therefore, incorporating machine learning in a cybersecurity learning factory can ease the complexity of cybersecurity training according to individuals' varying capabilities (Loftus & Narman, 2023) Simulation technology involves the use of computer software to replicate real-world scenarios and processes in a virtual environment.…”

“…In a cybersecurity learning factory, machine learning technology can be used to provide real-time and customised feedback to the user by detecting wrong inputs such as a syntax error in a programming language, interpreting that, and providing the most likely set of inputs a user may choose from to correct their input (Loftus & Narman, 2023). Personalised feedback has been proven to enhance learning, therefore, incorporating machine learning in a cybersecurity learning factory can ease the complexity of cybersecurity training according to individuals' varying capabilities (Loftus & Narman, 2023) Simulation technology involves the use of computer software to replicate real-world scenarios and processes in a virtual environment. In the context of learning factories, simulation has emerged as an effective tool for providing participants with a safe and controlled environment to experiment with various manufacturing processes.…”