Abstract:
In one embodiment, a capable node in a low power and lossy network (LLN) may monitor the authentication time for one or more nodes in the LLN. The capable node may dynamically correlate the authentication time with the location of the one or more nodes in the LLN in order to identify one or more authentication-delayed nodes. The node may then select, based on the location of the one or more authentication-delayed nodes, one or more key-delegation nodes to receive one or more network keys so that the key-delegation nodes may perform localized authentication of one or more of the authentication-delayed nodes. The capable node may then distribute the one or more network keys to the one or more key-delegation nodes.
Abstract:
In one embodiment, a device in a network performs anomaly detection functions using a machine learning-based anomaly detector to detect anomalous traffic in the network. The device identifies an ability of one or more nodes in the network to perform at least one of the anomaly detection functions. The device selects a particular one of the anomaly detection functions to offload to a particular one of the nodes, based on the ability of the particular node to perform the particular anomaly detection function. The device instructs the particular node to perform the selected anomaly detection function.
Abstract:
In one embodiment, a learning data processor determines a plurality of machine learning features in a computer network to collect. Upon receiving data corresponding to the plurality of features, the learning data processor may aggregate the data, and pushes the aggregated data for select features to interested learning machines associated with the computer network.
Abstract:
In one embodiment, a device in a network maintains information regarding anomaly detection models used in the network and applications associated with traffic analyzed by the anomaly detection models. The device receives an indication of a planned application deployment in the network. The device adjusts an anomaly detection strategy of a particular anomaly detector in the network based on the planned application deployment and on the information regarding anomaly detection models used in the network and the applications associated with the traffic analyzed by the anomaly detection models.
Abstract:
In one embodiment, nodes are polled in a network for Quality of Service (QoS) measurements, and a QoS anomaly that affects a plurality of potentially faulty nodes is detected based on the QoS measurements. A path, which traverses the plurality of potentially faulty nodes, is then computed from a first endpoint to a second endpoint. Also, a median node that is located at a point along the path between the first endpoint and the second endpoint is computed. Time-stamped packets are received from the median node, and the first endpoint and the second endpoint of the path are updated based on the received time-stamped packets, such that an amount of potentially faulty nodes is reduced. Then, the faulty node is identified from a reduced amount of potentially faulty nodes.
Abstract:
In one embodiment, a device (e.g., learning machine) determines a plurality of fate-sharing group (FSG) nodes in a computer network that are prone to simultaneously send an alarm upon detecting an event. As such, the device may elect one or more FSG owner nodes as a subset of the FSG nodes, and instructs the FSG group such that only FSG owner nodes send an alarm upon event detection.
Abstract:
In one embodiment, a first device in a network receives traffic flow data from a plurality of devices in the network. The traffic flow data from at least one of the plurality of devices comprises raw packets of a traffic flow. The first device selects a set of reporting devices from among the plurality of devices based on the received traffic flow data. The first device provides traffic flow reporting instructions to the selected set of reporting devices. The traffic flow reporting instructions cause each reporting device to provide sampled traffic flow data to an anomaly detection device.
Abstract:
In one embodiment, a packet to be transmitted along a communication path in a network from a source to a destination is determined, the communication path having one or more hops between the source and the destination. An instruction is sent to one or more tracking nodes along the communication path to track a number of local retransmissions required to successfully transmit the packet from each tracking node to a respective next-hop destination. Then, reports indicating the number of local retransmissions are received from the one or more tracking nodes.
Abstract:
In one embodiment, a device in a network receives an indication of a traffic shaping rate adjustment by a node due to a network condition. The device identifies a set of network nodes that are associated with the network condition. The device detects a traffic shaping rules violation by an offending node in the set of network nodes. The device sends an instruction that causes the offending node to use a different traffic shaping rate.
Abstract:
In one embodiment, a device in a network receives data indicative of traffic characteristics of traffic associated with a particular application. The device identifies one or more paths in the network via which the traffic associated with the particular application was sent, based on the traffic characteristics. The device determines a probing schedule based on the traffic characteristics. The probing schedule simulates the traffic associated with the particular application. The device sends probes along the one or more identified paths according to the determined probing schedule.