Abstract: In one embodiment, a service receives administration traffic data in a network associated with a remote administration session in which a control device remotely administers a client device. The service analyzes the administration traffic data to determine whether any portion of the administration traffic data is resulting from an administration session involving a trusted administrator. The service flags a first portion of the administration traffic data as authorized when the first portion of the administration traffic data is determined to result from an administration session involving a trusted administrator, and a second portion of the administration traffic data is non-flagged. The service assesses the second portion of the administration traffic data using a machine learning-based traffic classifier to determine whether the second portion of the administration traffic data is malicious.

Abstract: In one embodiment, a device in a network detects an encrypted traffic flow associated with a client in the network. The device captures contextual traffic data regarding the encrypted traffic flow from one or more unencrypted packets associated with the client. The device performs a classification of the encrypted traffic flow by using the contextual traffic data as input to a machine learning-based classifier. The device generates an alert based on the classification of the encrypted traffic flow.

Abstract: In one embodiment, a traffic inspection service executed by an intermediary device obtains, from a monitoring agent executed by an endpoint device, keying information for an encrypted traffic session between the endpoint device and a remote entity. The traffic inspection service provides a notification to the monitoring agent that acknowledges receipt of the keying information. The traffic inspection service uses the keying information to decrypt encrypted traffic from the encrypted traffic session. The traffic inspection service applies a policy to the encrypted traffic session between the endpoint device and the remote entity, based on the decrypted traffic from the session.

Abstract: According to an embodiment, a method receives one or more messages associated with connecting a client and a first host. At least one of the messages comprises an encrypted portion indicating the first host and at least one of the messages comprises a cleartext portion indicating a second host. The method determines first and second sets of links associated with the first and second host, respectively. The first set is determined based on monitoring a result of connecting the client and the first host. The second set is determined based on observing behavior associated with connecting to the second host. The method detects domain fronting in response to determining, based on comparing the first set of links and the second set of links, that the first host differs from the second host.

Abstract: In one embodiment, a device in a network receives certificate data for an encrypted traffic flow associated with a client node in the network. The device determines one or more data features from the certificate data. The device determines one or more flow characteristics of the encrypted traffic flow. The device performs a classification of an application executed by the client node and associated with the encrypted traffic flow by using a machine learning-based classifier to assess the one or more data features from the certificate data and the one or more flow characteristics of the traffic flow. The device causes performance of a network action based on a result of the classification of the application.

Abstract: In one embodiment, a device in a network receives an access policy and a class behavioral model for a node in the network that are associated with a class asserted by the node. The device applies the access policy and class behavioral model to traffic associated with the node. The device identifies a deviation in a behavior of the node from the class behavioral model, based on the application of the class behavioral model to the traffic associated with the node. The device causes performance of a mitigation action in the network based on the identified deviation in the behavior of the node from the class behavioral model.

Abstract: In one embodiment, a service receives captured traffic flow data regarding a traffic flow sent via a network between a first device assigned to a first network zone and a second device assigned to a second network zone. The service identifies, from the captured traffic flow data, one or more cryptographic parameters of the traffic flow. The service determines whether the one or more cryptographic parameters of the traffic flow satisfy an inter-zone policy associated with the first and second network zones. The service causes performance of a mitigation action in the network when the one or more cryptographic parameters of the traffic flow do not satisfy the inter-zone policy associated with the first and second network zones.

Abstract: In one embodiment, a device in a network receives an attack mitigation request regarding traffic in the network. The device causes an assessment of the traffic, in response to the attack mitigation request. The device determines that an attack detector associated with the attack mitigation request incorrectly assessed the traffic, based on the assessment of the traffic. The device causes an update to an attack detection model of the attack detector, in response to determining that the attack detector incorrectly assessed the traffic.

Abstract: According to certain embodiments, a method comprises performing a posture assessment at a trust anchor in order to determine whether a hardware component is authorized to run on a product. Performing the posture assessment comprises determining a random value (K), encrypting the random value (K) using a long-term key associated with the hardware component in order to yield an encrypted value, communicating the encrypted value to the hardware component, and determining whether the hardware component is authorized to run on the product based at least in part on whether the trust anchor receives, from the hardware component, a response encrypted using the random value (K). The method further comprises allowing or preventing the hardware component from running on the product based on whether the hardware component is authorized to run on the product.

Abstract: In one embodiment, a traffic analysis service that monitors a network obtains file metadata regarding an electronic file. The traffic analysis service determines a sensitivity score for the electronic file based on the file metadata. The traffic analysis service detects the electronic file within traffic in the network. The traffic analysis service causes performance of a mitigation action regarding the detection of the electronic file within the traffic, based on the sensitivity score of the electronic file.

Abstract: In one embodiment, a device in a network captures domain name system (DNS) response data from a DNS response sent by a DNS service to a client in the network. The device captures session data for an encrypted session of the client. The device makes a determination that the encrypted session is malicious by using the captured DNS response data and the captured session data as input to a machine learning-based or rule-based classifier. The device performs a mediation action in response to the determination that the encrypted session is malicious.

Abstract: According to certain embodiments, a method comprises receiving an encrypted value from a trust anchor. The encrypted value is received by a hardware component, and the encrypted value is associated with a posture assessment in which the trust anchor determines whether the hardware component is authorized to run on a product. The method further comprises obtaining a random value (K) based on decrypting the encrypted value. The decrypting uses a long-term key associated with the hardware component. The method further comprises communicating an encrypted response to the trust anchor. The encrypted response is encrypted using the random value (K). The encrypted response enables the trust anchor to determine whether the hardware component is authorized to run on the product.

Abstract: In one embodiment, a device in a network tracks traffic features indicated by header information of packets of an encrypted traffic flow over time. The encrypted traffic flow is associated with a particular host in the network. The device detects an operating system start event based on the traffic features and provides data regarding the detected operating system start event as input to a machine learning-based malware detector to determine whether the particular host with which the encrypted traffic flow is associated is infected with malware. The device causes performance of a mitigation action in the network when the malware detector determines that the particular host is infected with malware.

Abstract: In one embodiment, a telemetry exporter in a network establishes a tunnel between the telemetry exporter and a traffic analysis service. The telemetry exporter obtains packet copies of a plurality of packets sent between devices via the network. The telemetry exporter forms a set of traffic telemetry data by discarding at least a portion of one or more of the packet copies, based on a filter policy. The telemetry exporter applies compression to the formed set of traffic telemetry data. The telemetry exporter sends, via the tunnel, the compressed set of traffic telemetry data to the traffic analysis service for analysis.

Abstract: In one embodiment, a networking device in a network detects an traffic flow conveyed in the network via the networking device. The networking device generates flow data for the traffic flow. The networking device performs a classification of the traffic flow using the flow data as input to a machine learning-based classifier. The networking device performs a mediation action based on the classification of the traffic flow.

Abstract: In one embodiment, a device in a network receives domain name system (DNS) information for a domain. The DNS information includes one or more service tags indicative of one or more services offered by the domain. The device detects an encrypted traffic flow associated with the domain. The device identifies a service associated with the encrypted traffic flow based on the one or more service tags. The device prioritizes the encrypted traffic flow based on the identified service associated with the encrypted traffic flow.

Abstract: In one embodiment, a service receives administration traffic data in a network associated with a remote administration session in which a control device remotely administers a client device. The service analyzes the administration traffic data to determine whether any portion of the administration traffic data is resulting from an administration session involving a trusted administrator. The service flags a first portion of the administration traffic data as authorized when the first portion of the administration traffic data is determined to result from an administration session involving a trusted administrator, and a second portion of the administration traffic data is non-flagged. The service assesses the second portion of the administration traffic data using a machine learning-based traffic classifier to determine whether the second portion of the administration traffic data is malicious.

Abstract: A method comprises receiving, at a network infrastructure device, a flow of packets, determining, using the network infrastructure device and for a first subset of the packets, that the first subset corresponds to a first datagram and determining a first length of the first datagram, determining, using the network infrastructure device and for a second subset of the packets, that the second subset corresponds to a second datagram that was received after the first datagram, and determining a second length of the second datagram, determining, using the network infrastructure device, a duration value between a first arrival time of the first datagram and a second arrival time of the second datagram, sending, to a collector device that is separate from the network infrastructure device, the first length, the second length, and the duration value for analysis.

Abstract: In one embodiment, a device in a network receives traffic data regarding a plurality of observed traffic flows. The device maps one or more characteristics of the observed traffic flows from the traffic data to traffic characteristics associated with a targeted deployment environment. The device generates synthetic traffic data based on the mapped traffic characteristics associated with the targeted deployment environment. The device trains a machine learning-based traffic classifier using the synthetic traffic data.

Abstract: In one embodiment, a traffic analysis service receives captured traffic data regarding a Transport Layer Security (TLS) connection between a client and a server. The traffic analysis service applies a first machine learning-based classifier to TLS records from the traffic data, to identify a set of the TLS records that include Hypertext Transfer Protocol (HTTP) header information. The traffic analysis service estimates one or more HTTP transaction labels for the connection by applying a second machine learning-based classifier to the identified set of TLS records that include HTTP header information. The traffic analysis service augments the captured traffic data with the one or more HTTP transaction labels. The traffic analysis service causes performance of a network security function based on the augmented traffic data.