Cloud gaming applications have gained great adoption on the Internet particularly benefiting from the wide availability of broadband access networks. However, they still fail to meet users' quality requirements when accessed using cellular networks due to common wireless channel degradations. Machine Learning (ML) techniques can be leveraged to detect such anomalies during users' cloud gaming sessions. In this respect, unsupervised ML approaches are particularly interesting since they do not require labeled datasets. In this work, we investigate these approaches to understand their performance and their robustness. Our dataset consists of game sessions played on the public Google Stadia Cloud Gaming servers. The game sessions are played using a 4G network emulation replicating the capacity variations sampled on a commercial 4G network. We compare different models ranging from traditional approaches to deep learning and we evaluate their default performance while varying the level of contamination in their training datasets. Our experiments show that Auto-Encoders models achieve the best performance without contamination while the OC-SVM and the Isolation Forest are the most robust to data contamination.