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 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 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: 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 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 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: Methods and systems to estimate encrypted multi-path TCP (MPTCP) network traffic include restricting traffic in a first direction (e.g., uplink) to a single path, and estimating traffic of multiple subflows of a second direction (e.g., downlink) based on traffic over the single path of the first direction. The estimating may be based on, without limitation, acknowledgment information of the single path, a sequence of acknowledgment numbers of the single path, an unencrypted initial packet sent over the single path as part of a secure tunnel setup procedure, TCP header information of the unencrypted initial packet (e.g., sequence number, acknowledgment packet, and/or acknowledgment packet length), and/or metadata of packets of the single path (e.g., regarding cryptographic algorithms, Diffie-Helman groups, and/or certificate related data).

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: 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: In one embodiment, an apparatus captures a memory dump of a device in a sandbox environment executing a malware sample. The apparatus identifies a cryptographic key based on a particular data structure in the captured memory dump. The apparatus uses the identified cryptographic key to decrypt encrypted traffic sent by the device. The apparatus labels at least a portion of the decrypted traffic sent by the device as benign. The apparatus trains a machine learning-based traffic classifier based on the at least a portion of the decrypted traffic sent by the device and labeled as benign.

Abstract: In one embodiment, a device obtains one or more packets of a traffic session in a network. The device determines, for a particular packet of the one or more packets that match a filter, a fingerprint for the particular packet. The device identifies a plurality of traffic sessions whose packets match the fingerprint, wherein each of the plurality of traffic sessions is associated with at least one process. The device updates a process with the traffic session by applying a classifier to the plurality of traffic sessions.

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 obtains telemetry data regarding an encrypted traffic session in a network. The telemetry data includes Transport Layer Security (TLS) features of the traffic session and auxiliary information indicative of a destination address of the traffic session, a destination port of the traffic session, or a server name associated with the traffic session. The device retrieves, using the obtained telemetry data, a plurality of candidate processes from a TLS fingerprint database that relates processes with telemetry data from encrypted traffic sessions initiated by those processes. The device uses a probabilistic model to assign probabilities to each of the plurality of candidate processes. The device identifies one of the plurality of candidate processes as having initiated the encrypted traffic session based on its assigned probability.

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, an apparatus captures a memory dump of a device in a sandbox environment executing a malware sample. The apparatus identifies a cryptographic key based on a particular data structure in the captured memory dump. The apparatus uses the identified cryptographic key to decrypt encrypted traffic sent by the device. The apparatus labels at least a portion of the decrypted traffic sent by the device as benign. The apparatus trains a machine learning-based traffic classifier based on the at least a portion of the decrypted traffic sent by the device and labeled as benign.

Abstract: Methods an systems to classify a training dataset of network data as a poisoned training dataset based on a first dataset-level classifier, identify and remove poison samples of the poisoned training dataset based on a sample-level classifier to produce a non-poisoned dataset, training a machine-based model to analyze network traffic based on the modified non-poisoned dataset, and analyze network traffic with the machine-based model.

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 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.