A system and method for connected vehicle sequence anomaly detection. The method includes creating a normal sequence profile for a group of connected vehicles based on a plurality of first messages by training a normal behavior model using unsupervised machine learning with respect to potential sequences, the normal sequence profile defining normal sequences and triggers, wherein each of the plurality of normal sequences is associated with a timeframe, wherein each sequence is a series of condition combinations; preprocessing a second data set by generating a plurality of second messages in a unified format; identifying at least one instance of the plurality of triggers in the plurality of second messages; and detecting at least one abnormal sequence based on the identified at least one instance and the normal sequence profile, wherein an abnormal sequence is detected when none of the plurality of normal sequences is identified in the second data set.