Abstract: A method of storing information relating to the transmission of messages by an entity over a given time period comprises the step of creating a signature comprising a plurality of parameters related to the transmission of messages over that time period wherein the parameters comprise at least one parameter related to the transmission of messages over a portion of the period and also related to the position of the portion in the period, to enable output data to be derived from the stored information. The signature may be updated by a weighted averaging process with other more recent signatures. Application in fraud detection where signature representing information in many call detail records from a particular subscriber is fed to a neural network.

Abstract: A method of processing data relating to a plurality of examples using a data classifier arranged to classify input data into one of a number of classes, and a rule inducer, comprising the steps of: (i) inputting a series of inputs to the data classifier so as to obtain a series of corresponding outputs; (ii) inputting said series of outputs and at least some of said series of inputs to the rule inducer so as to obtain a series of rules which describe relationships between the series of inputs to the data classifier and the series of corresponding outputs from the data classifier.

Abstract: A method of storing information relating to the transmission of messages by an entity over a given time period comprises the step of creating a signature comprising a plurality of parameters related to the transmission of messages over that time period wherein the parameters comprise at least one parameter related to the transmission of messages over a portion of the period and also related to the position of the portion in the period, to enable output data to be derived from the stored information. The signature may be updated by a weighted averaging process with other more recent signatures. Application in fraud detection where signature representing information in many call detail records from a particular subscriber is fed to a neural network.

Abstract: A method of processing data relating to a plurality of examples using a data classifier arranged to classify input data into one of a number of classes, and a rule inducer, comprising the steps of:

Abstract: A computer product for analyzing data representative of account usage, the computer product disposed on a computer readable medium and including instructions for causing a processor to provide a data classifier the data representative of account usage, wherein the data classifier produces in response to the data representative of account usage, classification outputs, provide to a rule inducer input data based on the data representative of account usage and the classification outputs, wherein the rule inducer produces rules descriptive of relationships between the data representative of account usage and the classification outputs. The data classifier can include a neural network and a Bayesian classifier. The input data based on the data representative of account usage can include a combination of the data representative of account usage and the classification outputs.

Abstract: A method of storing information relating to the transmission of messages by an entity over a given time period comprises the step of

Abstract: A computer product for analyzing data representative of account usage, the computer product disposed on a computer readable medium and including instructions for causing a processor to provide a data classifier the data representative of account usage, wherein the data classifier produces in response to the data representative of account usage, classification outputs, provide to a rule inducer input data based on the data representative of account usage and the classification outputs, wherein the rule inducer produces rules descriptive of relationships between the data representative of account usage and the classification outputs. The data classifier can include a neural network and a Bayesian classifier. The input data based on the data representative of account usage can include a combination of the data representative of account usage and the classification outputs.

Abstract: A method of managing the processing of information using a first neural network, the information relating to the transmission of messages in a telecommunications network, uses the steps of:(i) monitoring the performance of the first neural network in processing the information;(ii) creating a second neural network of the same topology as the first when a predetermined performance threshold is reached, and(iii) retraining the second neural network while continuing to process the information using the first neural network. If the neural networks are implemented using objects, such retraining can be facilitated by using a persistance mechanism to enable the objects to be stored and moved. Applications in fraud detection.

Abstract: A generic anomaly detection engine is described which provides neural network technology to process information such as call detail records and detect anomalies in the information, such as mobile phone fraud. Information from call detail records is pre-processed to form signatures which represent the calling behavior of a subscriber. These signatures are formed in one of a plurality of predetermined formats. The generic anomaly detection engine is instantiated in order to create an anomaly detector which is suitable for a particular situation and during the instantiation process the topology of the neural network components is automatically adjusted to fit the required situation. The topology is adjusted to according to the format of the signatures.

Abstract: An arrangement for the detection of fraudulent use of a telephone subscriber's instrument in a mobile telephone system includes an input preprocessor (110), a neural network engine (111) coupled to the preprocessor, and an output postprocessor (112) coupled to the neural network engine. The preprocessor determines for each subscriber a first long term calling profile, a second short term calling profile, and a subscriber profile pattern comprising the difference between the first and second profiles. Each calling profile and subscriber profile pattern comprises a set of values for a respective set of call attributes. The neural network engine comprises a self organizing map trained to effect pattern recognition of the subscriber profile patterns and a multilayer perceptron adapted to determine for each recognized pattern a value indicative of the probability of a fraud being associated with that pattern.