  In this paper we explore the challenges and strategies for enhancing the
robustness of $k$-means clustering algorithms against adversarial
manipulations. We evaluate the vulnerability of clustering algorithms to
adversarial attacks, emphasising the associated security risks. Our study
investigates the impact of incremental attack strength on training, introduces
the concept of transferability between supervised and unsupervised models, and
highlights the sensitivity of unsupervised models to sample distributions. We
additionally introduce and evaluate an adversarial training method that
improves testing performance in adversarial scenarios, and we highlight the
importance of various parameters in the proposed training method, such as
continuous learning, centroid initialisation, and adversarial step-count.
