Evaluating the Security of Computer-Aided Learning Platforms Using a Fuzzy-Based Unified Computational Approach

The widespread use of Computer-Aided Learning Platforms (CALPs) has led to several cybersecurity issues, like illegal access, data breaches, privacy violations, insecure communications, and service disruptions. Deciding on a safe educational platform entails a complex multifactorial decision-making problem where security is dependent on several technical, organizational, and operational factors that are interconnected. Fuzzy logic, Analytical Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) have been individually studied previously for security analysis of CALPs, but integration of these methods has not yet been addressed in this domain. Hence, a novel decision-making framework that integrates fuzzy logic with AHP and TOPSIS is proposed in this paper for in-depth security inspection of CALPs. During the assessment, eight primary security attributes—authentication and access control, data privacy and protection, network and communication security, application security, system availability and reliability, monitoring and incident response, user awareness and security management, and emerging security technologies—are considered that further include sixteen sub-attributes. Following this, five popular CALPs, Moodle, Canvas LMS, Google Classroom, Blackboard Learn and Microsoft Teams for Education, are examined as alternatives. To summarize, the framework is proven to be an effective, precise, and secure method of ranking educational platforms based on the overall security performance. Multi-factor authentication is evaluated as the top-ranked attribute of security while Microsoft Teams for Education is ranked as the topmost priority when considering the given security artifacts. The sensitivity and comparison analyses with four different hybrid methods further demonstrate that the proposed framework is consistent and robust and should, therefore, be used as a useful decision-support tool for educational institutions, policymakers and system administrators in the process of picking CALPs that are safe and secure.