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:
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:
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:
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:
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.
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 detects a lowered performance of the machine learning-based model when a performance metric of the machine learning-based model is below a threshold for the performance metric. When it is determined that the lowered performance of the machine-learning based model is correlated with the sample rate of the received data, the service adjusts the sample rate of the data.
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.
Abstract:
In one embodiment, a device in a network receives feedback regarding an anomaly reporting mechanism used by the device to report network anomalies detected by a plurality of distributed learning agents to a user interface. The device determines an anomaly assessment rate at which a user of the user interface is expected to assess reported anomalies based in part on the feedback. The device receives an anomaly notification regarding a particular anomaly detected by a particular one of the distributed learning agents. The device reports, via the anomaly reporting mechanism, the particular anomaly to the user interface based on the determined anomaly assessment rate.
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.
Abstract:
In one embodiment, attack detectability metrics are received from nodes along a path in a network. The attack detectability metrics from the nodes along the path are used to compute a path attack detectability value. A determination is made as to whether the path attack detectability value satisfies a network policy and one or more routing paths in the network are adjusted based on the path attack detectability value not satisfying the network policy.