Machine learning (ML) models are increasingly used for time-series analysis to predict the behavior of complex dynamic systems, such as automatic process control systems and robotics. However, these models are vulnerable to various security threats, especially in adversarial environments. One prominent attack method is the Fast Gradient Sign Method (FGSM), which perturbs input data in a way that maximizes the model’s prediction error. This paper explores the unique vulnerabilities of ML models applied to time-series data, focusing on adversarial attacks (demonstrating influence of FGSM attack). Additionally, we discuss defense mechanisms to mitigate these risks and ensure the robustness of these models in safety-critical applications.