Business processes are bound to evolve as a form of adaption to changes, and such changes are referred as process drifts . Current process drift detection methods perform well on clean event log data, but the performance can be tremendously affected by noises . A good process drift detection method should be accurate, fast, and robust to noises . In this paper, we propose an offline process drift detection method which identifies each newly observed behaviour as a candidate drift point and checks if the new behaviour can signify significant changes to the original process behaviours . In addition, a bidirectional search method is proposed to accurately locate both the adding and removing of behaviours . The proposed method can accurately detect drift points from event logs and is robust to noises . Both artificial and real-life event logs are used to evaluate our method . Results show that our method can consistently report accurate process drift time while maintaining a reasonably fast detection speed.