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 traffic sent from a first endpoint. The device sends a padding request to the second endpoint indicative of a number of padding bytes. The device receives a padding response from the second endpoint, after sending the padding request to the second endpoint. The device adjusts the received traffic based on the received padding response by adding one or more frames to the received traffic. The device sends the adjusted traffic to the second endpoint.

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: 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: In one embodiment, a service receives traffic telemetry data regarding encrypted traffic sent by an endpoint device in a network. The service analyzes the traffic telemetry data to infer characteristics of an application on the endpoint device that generated the encrypted traffic. The service receives, from a monitoring agent on the endpoint device, application telemetry data regarding the application. The service determines that the application is evasive malware based on the characteristics of the application inferred from the traffic telemetry data and on the application telemetry data received from the monitoring agent on the endpoint device. The service initiates performance of a mitigation action in the network, after determining that the application on the endpoint device is evasive malware.

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 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: 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: 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 observes traffic between a client and a server for an encrypted session. The device makes a determination that a server certificate should be obtained from the server. The device, based on the determination, sends a handshake probe to the server. The device extracts server certificate information from a handshake response from the server that the server sent in response to the handshake probe. The device uses the extracted server certificate information to analyze the traffic between the client and the server.

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 service receives traffic telemetry data regarding encrypted traffic sent by an endpoint device in a network. The service analyzes the traffic telemetry data to infer characteristics of an application on the endpoint device that generated the encrypted traffic. The service receives, from a monitoring agent on the endpoint device, application telemetry data regarding the application. The service determines that the application is evasive malware based on the characteristics of the application inferred from the traffic telemetry data and on the application telemetry data received from the monitoring agent on the endpoint device. The service initiates performance of a mitigation action in the network, after determining that the application on the endpoint device is evasive malware.

Abstract: According to certain embodiments, a method performed by a trust anchor comprises determining a random value (K), encrypting the random value (K) using a long-term key associated with a hardware component in order to yield an encrypted value, communicating the encrypted value to the hardware component, and receiving a response encrypted using the random value (K). The response is received from the hardware component. The method further comprise encrypting a schema using the random value (K) and sending the encrypted schema to the hardware component. The schema indicates functionality that the hardware component is authorized to enable.

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: 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: 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 receiving, from the hardware component, a message encrypted using the random value (K). The message comprises an identifier associated with the hardware component. Performing the posture assessment further comprises determining whether the hardware component is authorized to run on the product based at least in part on the identifier associated with the hardware component. The method further comprises performing an action that depends on whether the hardware component is authorized to run on the product.

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 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 receives a set of known user identifiers used in the network. The device receives web traffic log data regarding web traffic in the network. The web traffic log data includes header information captured from the web traffic and a plurality of client addresses associated with the web traffic. The device detects a particular one of the set of known user identifiers in the header information captured from the web traffic associated with a particular one of the plurality of client addresses. The device makes an association between the particular detected user identifier and the particular client address.

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.