The rapid proliferation of bots in online environments poses a significant threat to the integrity and functionality of various platforms, ranging from social media networks to e-commerce websites. Currently, one of the most threatening bot attacks for airlines is Seat-Spinning - a fraudulent technique that exploits bots to initiate DoI attacks, resulting in significant losses for airline companies. The scope of this thesis is to propose an alerting system capable of detecting such attacks by leveraging flight inventory data. The problem will be modeled as an anomaly detection in time series in an online framework. The proposed pipeline utilizes reconstruction models, dynamic thresholding, and window scoring to perform the detection task on streaming data.