摘要:
In one embodiment, a device receives a classifier tracking request from a coordinator device that specifies a classifier verification time period. During the classifier verification time period, the device classifies a set of network traffic that includes traffic observed by the device and attack traffic specified by the coordinator device. The device generates classification results based on the classified set of network traffic and provides the classification results to the coordinator device.
摘要:
In one embodiment, a device in a network monitors a selective anomaly forwarding mechanism deployed in the network. The selective anomaly forwarding mechanism causes a participating node in the mechanism to selectively forward detected network anomalies to the device. The device monitors one or more resources of the network. The device determines an adjustment to the selective anomaly forwarding mechanism based on the one or more monitored resources of the network. The device implements the determined adjustment to the selective anomaly forwarding mechanism.
摘要:
In one embodiment, a device in a network identifies a set of traffic flow records that triggered an attack detector. The device selects a subset of the traffic flow records and calculates aggregated metrics for the subset. The device provides the aggregated metrics for the subset to the attack detector to generate an attack detection determination for the subset of traffic flow records. The device identifies one or more attack traffic flows from the set of traffic flow records based on the attack detection determination for the subset of traffic flow records.
摘要:
In one embodiment, a device in a network receives information regarding one or more attack detection service level agreements. The device identifies a set of attack detection classifiers as potential voters in a voting mechanism used to detect a network attack. The device determines one or more parameters for the voting mechanism based on the information regarding the one or more attack detection service level agreements. The device adjusts the voting mechanism used by the potential voters based on the one or more parameters for the voting mechanism.
摘要:
In one embodiment, data flows are received in a network, and information relating to the received data flows is provided to a machine learning attack detector. Then, in response to receiving an attack detection indication from the machine teaming attack detector, a traffic segregation procedure is performed including: computing an anomaly score for each of the received data flows based on a degree of divergence from an expected traffic model, determining a subset of the received data flows that have an anomaly score that is lower than or equal to an anomaly threshold value, and providing information relating to the subset of the received data flows to the machine learning attack detector.
摘要:
In one embodiment, a device in a network analyzes data regarding a detected anomaly in the network. The device determines whether the detected anomaly is a false positive. The device generates a white label for the detected anomaly based on a determination that the detected anomaly is a false positive. The device causes one or more alerts regarding the detected anomaly to be suppressed using the generated white label for the anomaly.
摘要:
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.
摘要:
In one embodiment, attack traffic corresponding to a detected DoS attack from one or more attacker nodes is received at a denial of service (DoS) attack management node in a network. The DoS attack management node determines attack information relating to the attack traffic, including a type of the DoS attack and an intended target of the DoS attack. Then, the DoS attack management node triggers an attack mimicking action based on the attack information, where the attack mimicking action mimics a behavior of the intended target of the DoS attack that would be expected by the one or more attacker nodes if the DoS attack were successful.
摘要:
In one embodiment, a device in a network detects a network attack using aggregated metrics for a set of traffic data. In response to detecting the network attack, the device causes the traffic data to be clustered into a set of traffic data clusters. The device causes one or more attack detectors to analyze the traffic data clusters. The device causes the traffic data clusters to be segregated into a set of one or more attack-related clusters and into a set of one or more clusters related to normal traffic based on an analysis of the clusters by the one or more attack detectors.
摘要:
In one embodiment, a device in a network receives an indication of a network anomaly detected by a first graph-based anomaly detection model hosted by a first node in the network. The device identifies one or more additional graph-based anomaly detection models based on the network anomaly detected by the first graph-based anomaly detection model. The device correlates one or more network events from the one or more additional graph-based anomaly detection models with the network anomaly detected by the first graph-based anomaly detection model. The device identifies a cause of the network anomaly using the one or more network events from the one or more additional graph-based anomaly detection models that are correlated with the network anomaly detected by the first graph-based anomaly detection model.