专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US11924048B2 Anomaly detection in computer networks 有权
公开(公告)号：US11924048B2
公开(公告)日：2024-03-05
申请号：US16619745
申请日：2018-06-08
申请人： British Telecommunications Public Limited Company
发明人： Maximilien Servajean , Yipeng Cheng
IPC分类号： H04L41/142 , G06F18/20 , G06F18/21 , G06F18/23 , G06N3/047 , H04L9/40
CPC分类号： H04L41/142 , G06F18/2193 , G06F18/23 , G06F18/295 , G06N3/047 , H04L63/1425
摘要： 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.

2. 发明授权

US11973778B2 Detecting anomalies in computer networks 有权
公开(公告)号：US11973778B2
公开(公告)日：2024-04-30
申请号：US17309528
申请日：2019-12-01
申请人： BRITISH TELECOMMUNICATIONS PUBLIC LIMITED COMPANY
发明人： Giulio Giaconi , Yipeng Cheng
IPC分类号： H04L9/40 , G06N3/08
CPC分类号： H04L63/1425 , G06N3/08 , H04L63/0245
摘要： 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 corresponding to the attribute value; repeating the accessing, the generating, the augmenting and the training to generate multiple generations of vector representations for each attribute value in the corpus, each generation corresponding to data records received during a different time period; and for at least a subset of attribute values in the corpus, comparing the multiple generations of vector representations to identify a change in one or more vector representation as an indication of an anomalous change of behavior in the computer network.

3. 发明授权

US11509671B2 Anomaly detection in computer networks 有权
公开(公告)号：US11509671B2
公开(公告)日：2022-11-22
申请号：US16619767
申请日：2018-06-08
申请人： British Telecommunications Public Limited Company
发明人： Maximilien Servajean , Yipeng Cheng
IPC分类号： H04L9/40 , G06N20/00 , G06N3/04 , G06N20/10
摘要： 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 filter for identifying subsequent time series for a second device being absent anomalous communication.

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式