Performance degradation in machine learning models can lead to significant losses in production environments, especially in communication systems. Concept drift, a phenomenon where the statistical properties of the target variable change over time, poses a critical challenge, making even initially effective models perform poorly in the long run. This paper compares various packet drop detection models to evaluate their resilience against concept drift. We also assess the performance of these models after applying drift reduction mechanisms to determine their effectiveness in maintaining robust performance over extended periods.