Abstract: A vehicle-to-everything (V2X) communication-based electronic toll collection system (ETCS) according to an embodiment of the present invention includes at least one roadside unit (RSU), a token issuer checking a de-identified token of a vehicle by communicating with an on-board unit (OBU) of the vehicle via the at least one roadside unit, and a clearing house communicating with the token issuer and charging a toll to a driver/owner of the vehicle. The token issuer generates toll information of the vehicle based on the de-identified token of the vehicle and location information of the at least one roadside unit involved in checking the de-identified token.

Abstract: Disclosed is a method and apparatus for detecting anomalies in a system log on the basis of self-supervised learning, using a language model. The method comprises performing preprocessing on the system log, generating a normal token sequence having a preset length by concatenating tokenized log lines of the system log, generating an abnormal token sequence using the normal token sequence, calculating an anomaly score for a determination target token sequence using a sentence classification model, and determining the token sequence as an abnormal system log when the calculated anomaly score is greater than a threshold value.