Abstract: 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.

Abstract: 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.

Abstract: A data processing system (20) has a central processing unit (CPU) (22) and a memory (30) for storing an exception table. The exception table is mapped in the memory (30) in consecutive segments, with each segment for storing a predetermined number of instructions for executing the exception. By asserting a control bit, the exception table can be relocated, or remapped, and compressed into a jump table. The jump table stores only jump instruction for branching to the exception routines, which are relocated to other memory locations. The jump table is generated from the starting addresses of the exception routines. Relocating the exception routines allows for more efficient use of internal memory space of the data processing system (20).