TLS fingerprinting for process identification

    公开(公告)号:US11223653B2

    公开(公告)日:2022-01-11

    申请号:US16512474

    申请日:2019-07-16

    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.

    Detection of malware and malicious applications

    公开(公告)号:US11057420B2

    公开(公告)日:2021-07-06

    申请号:US16370853

    申请日:2019-03-29

    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.

    Triggering targeted scanning to detect rats and other malware

    公开(公告)号:US11032314B2

    公开(公告)日:2021-06-08

    申请号:US16220115

    申请日:2018-12-14

    Abstract: In one embodiment, a security service classifies traffic telemetry data for traffic between an endpoint device and a server as potentially associated with a particular type of remote access Trojan (RAT). The security service constructs a scan message to elicit a type of server response associated with the particular type of RAT. The security service obtains a server response from the server, by sending the constructed scan message to the server. The security service determines whether the endpoint device is infected with the particular type of RAT, by validating whether the server response from the server matches the type of server response associated with the particular type of RAT.

    NETWORK TELEMETRY COLLECTION WITH PACKET METADATA FILTERING

    公开(公告)号:US20210160275A1

    公开(公告)日:2021-05-27

    申请号:US16693885

    申请日:2019-11-25

    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.

    PRIVACY ENHANCING MAN-IN-THE-MIDDLE
    117.
    发明申请

    公开(公告)号:US20210105301A1

    公开(公告)日:2021-04-08

    申请号:US16594203

    申请日:2019-10-07

    Abstract: In one embodiment, a device in a network intercepts traffic sent from a first endpoint destined for a second 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 intercepted traffic based on the received padding response. The device sends the adjusted traffic to the second endpoint.

    MALWARE CLASSIFICATION AND ATTRIBUTION THROUGH SERVER FINGERPRINTING USING SERVER CERTIFICATE DATA

    公开(公告)号:US20200267164A1

    公开(公告)日:2020-08-20

    申请号:US16869726

    申请日:2020-05-08

    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.

    Correlating endpoint and network views to identify evasive applications

    公开(公告)号:US10735441B2

    公开(公告)日:2020-08-04

    申请号:US15848150

    申请日:2017-12-20

    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.

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