In this research, we evaluate the effectiveness of different MTD techniques on the transformer-based cyber anomaly detection models trained on the KDD Cup’99 Dataset, a publicly available dataset commonly used for evaluating intrusion detection systems. We explore the trade-offs between security and performance when using MTD techniques for cyber anomaly detection and investigate how MTD techniques can be combined with other cybersecurity techniques to improve the overall security of the system. We evaluate their performance using standard metrics such as accuracy and FI score, as well as measures of robustness against adversarial attacks. Our results show that MTD techniques can significantly improve the security of the anomaly detection model, with some techniques being more effective than others depending on the model architecture. We also find that there are trade-offs between security and performance, with some MTD techniques leading to a reduction in model accuracy or an increase in computation time. However, we demonstrate that these tradeoffs can be mitigated by optimizing the MTD parameters for the specific model architecture.