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公开(公告)号：US09160760B2
公开(公告)日：2015-10-13
申请号：US14164475
申请日：2014-01-27
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Javier Cruz Mota , Andrea Di Pietro
CPC classification number: H04L63/1425 , G06N3/02 , G06N3/08 , G06N3/084 , G06N99/005 , H04L41/16 , H04L43/0876 , H04L45/48 , H04L47/127 , H04L47/2466 , H04L47/41
Abstract: In one embodiment, a training request is sent to a plurality of nodes in a network to cause the nodes to generate statistics regarding unicast and broadcast message reception rates associated with the nodes. The statistics are received from the nodes and a statistical model is generated using the received statistics and is configured to detect a network attack by comparing unicast and broadcast message reception statistics. The statistical model is then provided to the nodes and an indication that a network attack was detected by a particular node is received from the particular node.
Abstract translation: 在一个实施例中，训练请求被发送到网络中的多个节点，以使节点产生关于与节点相关联的单播和广播消息接收速率的统计。 从节点接收统计信息，并使用接收到的统计信息生成统计模型，并配置为通过比较单播和广播消息接收统计信息来检测网络攻击。 然后将统计模型提供给节点，并且从特定节点接收到特定节点检测到网络攻击的指示。
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公开(公告)号：US20150195216A1
公开(公告)日：2015-07-09
申请号：US14164507
申请日：2014-01-27
Applicant: Cisco Technology, Inc.
Inventor： Andrea Di Pietro , Jean-Philippe Vasseur , Javier Cruz Mota
IPC: H04L12/911 , H04L12/24 , H04W24/02 , H04L29/08 , H04W72/08 , H04B1/7156
CPC classification number: H04L41/5025 , H04L41/12 , H04L43/0852 , H04L45/02 , H04L45/70 , H04L47/122 , H04L47/365 , H04W24/02 , H04W24/04 , Y04S40/164
Abstract: In one embodiment, statistical information is collected relating to one or both of communication link quality or channel quality in a frequency-hopping network, in which packets are sent according to a frequency-hopping schedule that defines one or more timeslots, each timeslot corresponding to a transmission frequency. Also, a performance metric of a particular transmission frequency corresponding to a scheduled timeslot is predicted based on the collected statistical information. Based on the predicted performance metric, it is determined whether a transmitting node in the frequency-hopping network should transmit a packet during the scheduled timeslot using the particular transmission channel or wait until a subsequent timeslot to transmit the packet using another transmission frequency.
Abstract translation: 在一个实施例中，收集关于跳频网络中的通信链路质量或信道质量中的一个或两个的统计信息，其中根据定义一个或多个时隙的跳频调度发送分组，每个时隙对应于 传输频率。 此外，基于收集的统计信息来预测对应于调度时隙的特定传输频率的性能度量。 基于预测的性能度量，确定跳频网络中的发送节点是否应该在调度时隙期间使用特定传输信道发送分组，或者等待直到后续时隙来使用另一个传输频率来发送分组。
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公开(公告)号：US20150193694A1
公开(公告)日：2015-07-09
申请号：US14164446
申请日：2014-01-27
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Javier Cruz Mota , Andrea Di Pietro
CPC classification number: G06N99/005 , G06N3/0454 , G06N3/08 , G06N3/084 , H04L41/142 , H04L41/16 , H04L43/10 , H04L63/14 , H04L63/1408 , H04L63/1416
Abstract: In one embodiment, a first data set is received by a network device that is indicative of the statuses of a plurality of network devices when a type of network attack is not present. A second data set is also received that is indicative of the statuses of the plurality of network devices when the type of network attack is present. At least one of the plurality simulates the type of network attack by operating as an attacking node. A machine learning model is trained using the first and second data set to identify the type of network attack. A real network attack is then identified using the trained machine learning model.
Abstract translation: 在一个实施例中，当网络攻击的类型不存在时，第一数据集由网络设备接收，其指示多个网络设备的状态。 还接收当存在网络攻击的类型时指示多个网络设备的状态的第二数据集。 多个中的至少一个通过作为攻击节点操作来模拟网络攻击的类型。 使用第一和第二数据集来训练机器学习模型以识别网络攻击的类型。 然后使用训练有素的机器学习模型识别真实的网络攻击。
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14.发明授权公开(公告)号：US11769075B2
公开(公告)日：2023-09-26
申请号：US16548710
申请日：2019-08-22
Applicant: Cisco Technology, Inc.
Inventor： Erwan Barry Tarik Zerhouni , Abhishek Kumar , Javier Cruz Mota
IPC: G06N20/00 , G06Q10/067
CPC classification number: G06N20/00 , G06Q10/067
Abstract: The disclosed technology relates to a process of providing dynamic machine learning on premise model selection. In particular, a set of machine learned models are generated and provided to an on premise computing device. The machine learned models are generated using a cluster of customer data (e.g. telemetric data) stored on a computing network having different ranges of computational complexity. One of the machine learned models from the set of machine learned models will be selected based on the current available computational resources detected at the on premise computing device. Different machine learned models from the set of machine learned models can then be selected based on changes in the available computational resources and/or customer feedback.
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公开(公告)号：US20210144572A1
公开(公告)日：2021-05-13
申请号：US17152917
申请日：2021-01-20
Applicant: Cisco Technology, Inc.
Inventor： Javier Cruz Mota , Jean-Philippe Vasseur , Pierre-André Savalle , Grégory Mermoud
Abstract: In one embodiment, a device receives observed access point (AP) features of one or more APs in a monitored network. The device clusters the observed AP features within a latent space to form AP feature clusters. The device applies labels to the AP feature clusters within the latent space. The device uses the applied labels to the AP feature clusters to describe future behaviors of the one or more APs in the monitored network.
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Patent Agency Ranking
公开(公告)号：US20200322815A1
公开(公告)日：2020-10-08
申请号：US16905210
申请日：2020-06-18
Applicant: Cisco Technology, Inc.
Inventor： Pierre-André Savalle , Grégory Mermoud , Jean-Philippe Vasseur , Javier Cruz Mota
Abstract: In one embodiment, a device receives data regarding usage of access points in a network by a plurality of clients in the network. The device maintains an access point graph that represents the access points in the network as vertices of the access point graph. The device generates, for each of the plurality of clients, client trajectories as trajectory subgraphs of the access point graph. A particular client trajectory for a particular client comprises a set of edges between a subset of the vertices of the access point graph and represents transitions between access points in the network performed by the particular client. The device identifies a transition pattern from the client trajectories by deconstructing the trajectory subgraphs. The device uses the identified transition pattern to effect a configuration change in the network.
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公开(公告)号：US10356111B2
公开(公告)日：2019-07-16
申请号：US14164467
申请日：2014-01-27
Applicant: Cisco Technology, Inc.
Inventor： Andrea Di Pietro , Jean-Philippe Vasseur , Javier Cruz Mota
IPC: G06N3/02 , G06N3/08 , G06N20/00 , H04L12/24 , H04L29/06 , H04L12/26 , H04L12/801 , H04L12/855 , H04L12/891 , H04L12/753
Abstract: In one embodiment, a device evaluates a set of training data for a machine learning model to identify a missing feature subset in a feature space of the set of training data. The device identifies a plurality of network nodes eligible to initiate an attack on a network to generate the missing feature subset. One or more attack nodes are selected from among the plurality of network nodes. An attack routine is provided to the one or more attack nodes to cause the one or more attack nodes to initiate the attack. An indication that the attack has completed is then received from the one or more attack nodes.
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公开(公告)号：US20190207822A1
公开(公告)日：2019-07-04
申请号：US15860017
申请日：2018-01-02
Applicant: Cisco Technology, Inc.
Inventor： Andrea Di Pietro , Jean-Philippe Vasseur , Javier Cruz Mota
Abstract: In one embodiment, a network assurance service receives, from a reporting entity, data regarding a monitored network for input to a machine learning-based analyzer of the network assurance service. The service forms a reporting entity model of the reporting entity, based on at least a portion of the data received from the reporting entity. The service identifies a behavioral change of the reporting entity by comparing a sample of the data received from the reporting entity to the reporting entity model. The service correlates the behavioral change of the reporting entity to a change made to the reporting entity. The service causes performance of a mitigation action, to prevent the behavioral change from affecting operation of the machine learning-based analyzer.
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19.发明申请公开(公告)号：US20190171169A1
公开(公告)日：2019-06-06
申请号：US15831482
申请日：2017-12-05
Applicant: Cisco Technology, Inc.
Inventor： Andrea Di Pietro , Jean-Philippe Vasseur , Javier Cruz Mota
CPC classification number: G05B13/045 , G05B13/04 , H04L41/12 , H04L41/16 , H04L43/024 , H04L43/16
Abstract: In one embodiment, a network assurance service receives data regarding a monitored network. The service analyzes the received data using a machine learning-based model, to perform a network assurance function for the monitored network. The service determines that performance of the model is negatively affected by a sample rate of the received data. The service adjusts the sample rate of the data, based on the determination that the performance of the model is negatively affected by the sample rate of the received data.
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公开(公告)号：US20180365581A1
公开(公告)日：2018-12-20
申请号：US15704595
申请日：2017-09-14
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Pierre-André Savalle , Javier Cruz Mota
Abstract: In one embodiment, a service uses a set of collected characteristics of a client device in a network as input to a machine learning-based model that predicts a quality score for an online conference in which the client device is a participant. The service determines a resource consumption by the client device or the network that is associated with collecting the characteristics of the client device. The service determines an efficacy of the machine learning-based model as a function of the set of collected characteristics of the client device. The service adjusts the set of collected characteristics of the client device to optimize the efficacy of the model and the resource consumption associated with collecting the characteristics of the client device.
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