In recent years, advances in Distributed Acoustic Sensing (DAS) technology have resulted in significant progress in the detection of vibration sources. However, its use in railway monitoring is still relatively new, even though thousands of kilometers of optical fiber cables are already set up for telecommunication purposes, thus potentially exploitable. In this paper, we explore the possibility of using a DAS system and machine learning tools to detect rail defects along the track. Rail defects are defined as anything other than a smooth rail, and we focus on the detection of rail joints, which are common elements along the track. In this study, measurements were carried out on a short railway section of a few kilometers between two train stations in Paris. The results show that nearly all rail joints along the track are correctly detected, demonstrating the ability of the system to detect these elements with a spatial accuracy of a few meters. Lastly, some future perspectives for the study are proposed, such as a more in-depth analysis of the detected locations or the integration of field information to enhance the reliability of detections.