Distributed voting mechanism for attack detection
    51.
    发明授权
    Distributed voting mechanism for attack detection 有权
    分布式投票机制进行攻击检测

    公开(公告)号:US09230104B2

    公开(公告)日:2016-01-05

    申请号:US14273676

    申请日:2014-05-09

    CPC classification number: G06F21/554 H04L63/1408 H04W12/12 H04W84/18

    Abstract: In one embodiment, a network node receives a voting request from a neighboring node that indicates a potential network attack. The network node determines a set of feature values to be used as input to a classifier based on the voting request. The network node also determines whether the potential network attack is present by using the set of feature values as input to the classifier. The network node further sends a vote to the neighboring node that indicates whether the potential network attack was determined to be present.

    Abstract translation: 在一个实施例中,网络节点从指示潜在网络攻击的相邻节点接收投票请求。 网络节点基于投票请求确定要用作分类器的输入的一组特征值。 网络节点还通过使用一组特征值作为分类器的输入来确定潜在的网络攻击是否存在。 网络节点还向相邻节点发送表示是否确定潜在网络攻击存在的投票。

    GROUND TRUTH EVALUATION FOR VOTING OPTIMIZATION
    52.
    发明申请
    GROUND TRUTH EVALUATION FOR VOTING OPTIMIZATION 有权
    投票优化的地面真相评估

    公开(公告)号:US20150334123A1

    公开(公告)日:2015-11-19

    申请号:US14278532

    申请日:2014-05-15

    Abstract: In one embodiment, attack observations by a first node are provided to a user interface device regarding an attack detected by the node. Input from the user interface device is received that confirms that a particular attack observation by the first node indicates that the attack was detected correctly by the first node. Attack observations by one or more other nodes are provided to the user interface device. Input is received from the user interface device that confirms whether the attack observations by the first node and the attack observations by the one or more other nodes are both related to the attack. The one or more other nodes are identified as potential voters for the first node in a voting-based attack detection mechanism based on the attack observations from the first node and the one or more other nodes being related.

    Abstract translation: 在一个实施例中,第一节点的攻击观察被提供给用户接口设备关于由该节点检测到的攻击。 接收到来自用户界面设备的输入,其确认第一节点的特定攻击观察指示第一节点正确地检测到攻击。 一个或多个其他节点的攻击观察被提供给用户界面设备。 从用户接口设备接收输入,确认第一节点的攻击观察和一个或多个其他节点的攻击观察是否与攻击有关。 基于来自第一节点和一个或多个其他相关节点的攻击观察,基于投票的攻击检测机制中的一个或多个其他节点被识别为第一节点的潜在选民。

    VOTING STRATEGY OPTIMIZATION USING DISTRIBUTED CLASSIFIERS
    53.
    发明申请
    VOTING STRATEGY OPTIMIZATION USING DISTRIBUTED CLASSIFIERS 审中-公开
    使用分布式分类器投票策略优化

    公开(公告)号:US20150326450A1

    公开(公告)日:2015-11-12

    申请号:US14275344

    申请日:2014-05-12

    Abstract: In one embodiment, voting optimization requests that identify a validation data set are sent to a plurality of network nodes. Voting optimization data is received from the plurality of network nodes that was generated by executing classifiers using the validation data set. A set of one or more voting classifiers is then selected from among the classifiers based on the voting optimization data. One or more network nodes that host a voting classifier in the set of one or more selected voting classifiers is then notified of the selection.

    Abstract translation: 在一个实施例中,将识别验证数据集的投票优化请求发送到多个网络节点。 从通过使用验证数据集执行分类器生成的多个网络节点接收投票优化数据。 然后基于投票优化数据从分类器中选择一组一个或多个投票分类器。 然后通知一个或多个所选投票分类器的集合中的投票分类器的一个或多个网络节点。

    CROSS-VALIDATION OF A LEARNING MACHINE MODEL ACROSS NETWORK DEVICES
    54.
    发明申请
    CROSS-VALIDATION OF A LEARNING MACHINE MODEL ACROSS NETWORK DEVICES 有权
    通过网络设备的学习机器模型的交叉验证

    公开(公告)号:US20150193697A1

    公开(公告)日:2015-07-09

    申请号:US14164482

    申请日:2014-01-27

    Abstract: In one embodiment, a first network device receives a notification that the first network device has been selected to validate a machine learning model for a second network device. The first network device receives model parameters for the machine learning model that were generated by the second network device using training data on the second network device. The model parameters are used with local data on the first network device to determine performance metrics for the model parameters. The performance metrics are then provided to the second network device.

    Abstract translation: 在一个实施例中,第一网络设备接收第一网络设备已经被选择以验证第二网络设备的机器学习模型的通知。 第一网络设备接收由第二网络设备使用第二网络设备上的训练数据生成的机器学习模型的模型参数。 模型参数与第一个网络设备上的本地数据一起使用,以确定模型参数的性能指标。 然后将性能度量提供给第二网络设备。

    ANALYZING THE IMPACT OF NETWORK EVENTS ACROSS TIME

    公开(公告)号:US20210067430A1

    公开(公告)日:2021-03-04

    申请号:US16560748

    申请日:2019-09-04

    Abstract: The present technology pertains to a system, method, and non-transitory computer-readable medium for evaluating the impact of network changes. The technology can detect a temporal event, wherein the temporal event is associated with a change in a network configuration, implementation, or utilization. The technology defines, based on a nature of the temporal event, a first period prior to the temporal event or a second period posterior to the temporal event. The technology compares network data collected in the first period and network data collected in the second period.

    DESIGNATING A VOTING CLASSIFIER USING DISTRIBUTED LEARNING MACHINES

    公开(公告)号:US20200007412A1

    公开(公告)日:2020-01-02

    申请号:US16564176

    申请日:2019-09-09

    Abstract: In one embodiment, possible voting nodes in a network are identified. The possible voting nodes each execute a classifier that is configured to select a label from among a plurality of labels based on a set of input features. A set of one or more eligible voting nodes is selected from among the possible voting nodes based on a network policy. Voting requests are then provided to the one or more eligible voting nodes that cause the one or more eligible voting nodes to select labels from among the plurality of labels. Votes are received from the eligible voting nodes that include the selected labels and are used to determine a voting result.

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