Trust-Enabled Framework for Smart Classroom Ransomware Detection: Advancing Educational Cybersecurity Through Crowdsourcing

The proliferation of e-learning has exposed smart classroom devices and online learning platforms to ransomware attacks, threatening the integrity of educational processes. This study introduced a novel trust-based crowdsourcing framework to mitigate such attacks in smart classrooms. We evaluated our framework using two trust management algorithms, EigenTrust and Trust Network Analysis with Subjective Logic (TNaSL), comparing them against a baseline scenario without trust management. Experimental results, based on success rate, accuracy, precision, and recall metrics, demonstrated the significant enhancement of security in crowdsourcing processes. Both implementations exhibited resilience against increasing proportions of malicious nodes. This study contributes to cybersecurity in smart educational settings by demonstrating the efficacy of trust-based crowdsourcing in ransomware detection. Our framework paves the way for more secure digital learning spaces, addressing the cybersecurity challenges in IoT-enabled educational environments.