INNOVATIVE NEURAL NETWORK STRATEGIES FOR DETECTING CHEATING IN VIRTUAL EXAMS
Keywords:
Graph Neural Networks (GNNs), Federated Learning (FL), Collaborative Cheating Detection, Privacy-Preserving AI, Virtual Exam Proctoring, Ethical AI in Education, Synthetic Data GenerationAbstract
The rise of virtual exams has introduced new challenges in maintaining academic integrity, particularly in detecting collaborative cheating. In this study, we propose a novel framework that combines Graph Neural Networks (GNNs) with Federated Learning (FL) to detect collaborative cheating while preserving student privacy. Our approach models student interactions as a graph, where nodes represent students and edges represent potential cheating relationships. By training the GNN in a federated manner, we ensure that sensitive data remains on students' devices, addressing ethical and legal concerns.
References
References
Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., & Yu, P. S. (2020). Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. arXiv preprint arXiv:2008.08692. Retrieved from https://arxiv.org/abs/2008.08692
Kim, H., Choi, J., & Whang, J. J. (2023). Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection. arXiv preprint arXiv:2310.04171. Retrieved from https://arxiv.org/abs/2310.04171
Kanagavelu, R., Li, Z., Samsudin, J., Yang, Y., Yang, F., Goh, R. S. M., Cheah, M., Wiwatphonthana, P., Akkarajitsakul, K., & Wang, S. (2020). Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning. arXiv preprint arXiv:2005.11901. Retrieved from https://arxiv.org/abs/2005.11901
Stojkovic, B., Woodbridge, J., Fang, Z., Cai, J., Petrov, A., Iyer, S., Huang, D., Yau, P., Kumar, A. S., & Jawa, H. (2022). Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings. arXiv preprint arXiv:2206.00807. Retrieved from https://arxiv.org/abs/2206.00807
NVIDIA Developer. (n.d.). Supercharging Fraud Detection in Financial Services with Graph Neural Networks. Retrieved from https://developer.nvidia.com/blog/supercharging-fraud-detection-in-financial-services-with-graph-neural-networks/
Idrees, H. (2023, June 15). Federated Learning: Privacy-Preserving Machine Learning. Medium. Retrieved from https://medium.com/@hassaanidrees7/federated-learning-privacy-preserving-machine-learning-8d2fadfdd6e5
National Institute of Standards and Technology (NIST). (2023, April 10). The UK-US Blog Series on Privacy-Preserving Federated Learning: Introduction. Retrieved from https://www.nist.gov/blogs/cybersecurity-insights/uk-us-blog-series-privacy-preserving-federated-learning-introduction
Amazon Web Services (AWS). (2022, May 5). Detect financial transaction fraud using a Graph Neural Network with Amazon SageMaker. Retrieved from https://aws.amazon.com/blogs/machine-learning/detect-financial-transaction-fraud-using-a-graph-neural-network-with-amazon-sagemaker/
Heintz, M. (2024, November 21). Graph Neural Networks: Fraud Detection and Protein Function Prediction. Towards Data Science. Retrieved from https://towardsdatascience.com/graph-neural-networks-fraud-detection-and-protein-function-prediction-08f9531c98de/
Wikipedia contributors. (2025, January 15). Federated learning. Wikipedia, The Free Encyclopedia. Retrieved from https://en.wikipedia.org/wiki/Federated_learning
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Copyright (c) 2025 Jasurbek Abdullayev, Otabek Ergashev
This work is licensed under a Creative Commons Attribution 4.0 International License.
