Disclosed is a method for detecting anomaly traffic using an autoencoder based on an inception-residual network-v2 which is one type of machine learning methods, the method including generating a plurality of encoders on the basis of traffic data for learning; generating a plurality of pieces of image data on the basis of traffic data for each session, which is a detection target; and determining whether the traffic data for each session is abnormal using at least one among the traffic data for learning, the plurality of encoders, and the plurality of pieces of image data.