Patents by Inventor Yipeng CHENG
Yipeng CHENG has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11973778Abstract: A computer implemented method of detecting anomalous behavior within a computer network, the method including accessing data records each corresponding to an occurrence of communication occurring via the computer network and including a plurality of attributes of the communication; generating, for each of at least a subset of the data records, a training data item for a neural network, the training data item being derived from at least a portion of the attributes of the record and the neural network having input units and output units corresponding to items in a corpus of attribute values for communications occurring via the network; augmenting the training data by replicating each of one or more training data items responsive to one or more attributes of the data record corresponding to the training data item; training the neural network using the augmented training data so as to define a vector representation for each attribute value in the corpus based on weights in the neural network for an input unit corType: GrantFiled: December 1, 2019Date of Patent: April 30, 2024Assignee: British Telecommunications Public Limited CompanyInventors: Giulio Giaconi, Yipeng Cheng
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Patent number: 11924048Abstract: A method of anomaly detection for network traffic communicated by devices via a computer network, the method including clustering a set of time series, each time series including a plurality of time windows of data corresponding to network communication characteristics for a device; training an autoencoder for each cluster based on time series in the cluster; generating a set of reconstruction errors for each autoencoder based on testing the autoencoder with data from time windows of at least a subset of the time series; generating a probabilistic model of reconstruction errors for each autoencoder; and generating an aggregation of the probabilistic models for, in use, detecting reconstruction errors for a time series of data corresponding to network communication characteristics for a device as anomalous.Type: GrantFiled: June 8, 2018Date of Patent: March 5, 2024Assignee: British Telecommunications Public Limited CompanyInventors: Maximilien Servajean, Yipeng Cheng
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Publication number: 20220407884Abstract: A computer implemented method of computer security for a network-connected device communicating via a computer network, by accessing one or more attributes of communication over the network by the device, the communication according with one or more service discovery protocols; classifying the device based on the attributes, the classification having associated a predetermined set of acceptable states of operation of the device; deploying security measures for the device responsive to a detection of a deviation of a state of operation of the device from the acceptable states of operation, wherein the classification is made using a supervised machine learning method trained using training data for a plurality of training network-connected devices each having associated one or more attributes of communication over a network according with the one or more service discovery protocols, and each device having associated a definition of a set of acceptable states of operation.Type: ApplicationFiled: November 10, 2020Publication date: December 22, 2022Inventors: Fadi EL-MOUSSA, Yipeng CHENG
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Patent number: 11509671Abstract: A method of anomaly detection for network traffic communicated by devices via a computer network, the method including receiving a set of training time series each including a plurality of time windows of data corresponding to network communication characteristics for a first device; training an autoencoder for a first cluster based on a time series in the first cluster, wherein a state of the autoencoder is periodically recorded after a predetermined fixed number of training examples to define a set of trained autoencoders for the first cluster; receiving a new time series including a plurality of time windows of data corresponding to network communication characteristics for the first device; for each time window of the new time series, generating a vector of reconstruction errors for the first device for each autoencoder based on testing the autoencoder with data from the time window; and evaluating a derivative of each vector; training a machine learning model based on the derivatives so as to define a fiType: GrantFiled: June 8, 2018Date of Patent: November 22, 2022Assignee: British Telecommunications Public Limited CompanyInventors: Maximilien Servajean, Yipeng Cheng
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Publication number: 20220060492Abstract: A computer implemented method of detecting anomalous behavior within a computer network, the method including accessing data records each corresponding to an occurrence of communication occurring via the computer network and including a plurality of attributes of the communication; generating, for each of at least a subset of the data records, a training data item for a neural network, the training data item being derived from at least a portion of the attributes of the record and the neural network having input units and output units corresponding to items in a corpus of attribute values for communications occurring via the network; augmenting the training data by replicating each of one or more training data items responsive to one or more attributes of the data record corresponding to the training data item; training the neural network using the augmented training data so as to define a vector representation for each attribute value in the corpus based on weights in the neural network for an input unit corType: ApplicationFiled: December 1, 2019Publication date: February 24, 2022Inventors: Giulio GIACONI, Yipeng CHENG
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Publication number: 20200210782Abstract: A method of anomaly detection for network traffic communicated by devices via a computer network, the method including clustering a set of time series, each time series including a plurality of time windows of data corresponding to network communication characteristics for a device; training an autoencoder for each cluster based on time series in the cluster; generating a set of reconstruction errors for each autoencoder based on testing the autoencoder with data from time windows of at least a subset of the time series; generating a probabilistic model of reconstruction errors for each autoencoder; and generating an aggregation of the probabilistic models for, in use, detecting reconstruction errors for a time series of data corresponding to network communication characteristics for a device as anomalous.Type: ApplicationFiled: June 8, 2018Publication date: July 2, 2020Applicant: British Telecommunications Public Limited CompanyInventors: Maximilien SERVAJEAN, Yipeng CHENG
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Publication number: 20200106795Abstract: A method of anomaly detection for network traffic communicated by devices via a computer network, the method including receiving a set of training time series each including a plurality of time windows of data corresponding to network communication characteristics for a first device; training an autoencoder for a first cluster based on a time series in the first cluster, wherein a state of the autoencoder is periodically recorded after a predetermined fixed number of training examples to define a set of trained autoencoders for the first cluster; receiving a new time series including a plurality of time windows of data corresponding to network communication characteristics for the first device; for each time window of the new time series, generating a vector of reconstruction errors for the first device for each autoencoder based on testing the autoencoder with data from the time window; and evaluating a derivative of each vector; training a machine learning model based on the derivatives so as to define a fiType: ApplicationFiled: June 8, 2018Publication date: April 2, 2020Applicant: British Telecommunications Public Limited CompanyInventors: Maximilien SERVAJEAN, Yipeng CHENG