Abstract: In one embodiment, a device in a network captures a first set of packets based on first packet capture criterion. The captured first set of packets is provided for deep packet inspection and anomaly detection. The device receives a second packet capture criterion that differs from the first packet capture criterion. The device captures a second set of packets based on the second packet capture criterion. The device provides the captured second set of packets for deep packet inspection and anomaly detection. The anomaly detection of the captured first and second sets of packets is performed by a machine learning-based anomaly detector configured to generate anomaly detection results based in part on one or more traffic metrics gathered from the network and based further in part on deep packet inspection results of packets captured in the network.

Abstract: 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.

Abstract: 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.

Abstract: In one embodiment, a traffic model manager node receives data flows in a network and determines a degree to which the received data flows conform to one or more traffic models classifying particular types of data flows as non-malicious. If the degree to which the received data flows conform to the one or more traffic models is sufficient, the traffic model manager node characterizes the received data flows as non-malicious. Otherwise, the traffic model manager node provides the received data flows to a denial of service (DoS) attack detector in the network to allow the received data flows to be scanned for potential attacks.

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: In one embodiment, a device in a network identifies a universal resource locator (URL) from traffic destined for the URL that triggered a first anomaly detected by an anomaly detector. The device reports the first anomaly and the identified URL to a supervisory device in the network. The device receives a URL filter rule for the URL. The URL filter rule is configured to affect anomaly scores generated by the anomaly detector for traffic destined for the URL or a domain associated with the URL. The device uses the URL filter rule to adjust an anomaly score for a second anomaly detected by the anomaly detector based on the second anomaly involving traffic destined for the URL or the domain associated with the URL.

Abstract: In one embodiment, a supervisory device in a network receives traffic data from a security device that uses traffic signatures to assess traffic in the network. The supervisory device receives traffic data from one or more distributed learning agents that use machine learning-based anomaly detection to assess traffic in the network. The supervisory device trains a traffic classifier using the received traffic data from the security device and from the one or more distributed learning agents. The supervisory device deploys the traffic classifier to a selected one of the one or more distributed learning agents.

Abstract: In one embodiment, a device in a network receives, from a supervisory device, trace information for one or more traffic flows associated with a particular anomaly. The device remaps network addresses in the trace information to addresses of one or more nodes in the network based on roles of the one or more nodes. The device mixes, using the remapped network addresses, the trace information with traffic information regarding one or more observed traffic flows in the network, to form a set of mixed traffic information. The device analyzes the mixed traffic information using an anomaly detection model. The device provides an indication of a result of the analysis of the mixed traffic information to the supervisory device.

Abstract: In one embodiment, a primary networking device in a branch network receives a notification of an anomaly detected by a secondary networking device in the branch network. The primary networking device is located at an edge of the network. The primary networking device aggregates the anomaly detected by the secondary networking device and a second anomaly detected in the network into an aggregated anomaly. The primary networking device associates the aggregated anomaly with a location of the secondary networking device in the branch network. The primary networking device reports the aggregated anomaly and the associated location of the secondary networking device to a supervisory device.

Abstract: In one embodiment, a networking device in a network causes formation of device clusters of devices in the network. The devices in a particular cluster exhibit similar characteristics. The networking device receives feedback from a device identity service regarding the device clusters. The feedback is based in part on the device identity service probing the devices. The networking device adjusts the device clusters based on the feedback from the device identity service. The networking device performs anomaly detection in the network using the adjusted device clusters.

Abstract: In one embodiment, a node in a network detects an anomaly in the network based on a result of a machine learning-based anomaly detector analyzing network traffic. The node determines a packet capture policy for the anomaly by applying a machine learning-based classifier to the result of the anomaly detector. The node selects a set of packets from the analyzed traffic based on the packet capture policy. The node stores the selected set of packets for the detected anomaly.

Abstract: In one embodiment, a first device in a network identifies a first traffic flow between two endpoints that traverses the first device in a first direction. The first device receives information from a second device in the network regarding a second traffic flow between the two endpoints that traverses the second device in a second direction that is opposite that of the first direction. The first device merges characteristics of the first traffic flow captured by the first device with characteristics of the second traffic flow captured by the second device and included in the information received from the second device, to form an input feature set. The first device detects an anomaly in the network by analyzing the input feature set using a machine learning-based anomaly detector.

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 device in a network generates an expected traffic model based on a training set of data used to train a machine learning attack detector. The device provides the expected traffic model to one or more nodes in the network. The device receives an unexpected behavior notification from a particular node of the one or more nodes. The particular node generates the unexpected behavior notification based on a comparison between the expected traffic model and an observed traffic behavior by the node. The particular node also prevents the machine learning attack detector from analyzing the observed traffic behavior. The device updates the machine learning attack detector to account for the observed traffic behavior.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In one embodiment, a device in a network captures a first set of packets based on first packet capture criterion. The captured first set of packets is provided for deep packet inspection and anomaly detection. The device receives a second packet capture criterion that differs from the first packet capture criterion. The device captures a second set of packets based on the second packet capture criterion. The device provides the captured second set of packets for deep packet inspection and anomaly detection. The anomaly detection of the captured first and second sets of packets is performed by a machine learning-based anomaly detector configured to generate anomaly detection results based in part on one or more traffic metrics gathered from the network and based further in part on deep packet inspection results of packets captured in the network.