Abstract: The invention provides a method for learning and identification of message types in a network in which the learning and identification is based solely upon information contained in messages in the network. In one embodiment, the method includes comparing a current message with one or more previously defined message structures and applying a comparison method in order to infer whether the structure of the current message conforms to any one of the previously defined message structures. The invention also provides a method for classifying a collection of messages input or output from a computational node in which the set of messages is portioned into subsets of similar messages, and a structure of the messages of each subset is characterized. The invention further provides a method for inferring causal relations between a message type input to a computational node in a network and message type output from the computational node in the network using one or more statistical tests.

Abstract: The invention provides a method for learning and identification of message types in a network in which the learning and identification is based solely upon information contained in messages in the network. In one embodiment, the method includes comparing a current message with one or more previously defined message structures and applying a comparison method in order to infer whether the structure of the current message conforms to any one of the previously defined message structures. The invention also provides a method for classifying a collection of messages input or output from a computational node in which the set of messages is portioned into subsets of similar messages, and a structure of the messages of each subset is characterized. The invention further provides a method for inferring causal relations between a message type input to a computational node in a network and message type output from the computational node in the network using one or more statistical tests.