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.

    ASSOCIATING A USER IDENTIFIER DETECTED FROM WEB TRAFFIC WITH A CLIENT ADDRESS

    公开(公告)号:US20220345470A1

    公开(公告)日:2022-10-27

    申请号:US17861583

    申请日:2022-07-11

    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.

    MALWARE CLASSIFICATION AND ATTRIBUTION THROUGH SERVER FINGERPRINTING USING SERVER CERTIFICATE DATA

    公开(公告)号:US20210377283A1

    公开(公告)日:2021-12-02

    申请号:US17395968

    申请日:2021-08-06

    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.

    SUPERVISED LEARNING SYSTEM
    7.
    发明申请

    公开(公告)号:US20190258965A1

    公开(公告)日:2019-08-22

    申请号:US15901915

    申请日:2018-02-22

    Abstract: In one embodiment, a method including accessing a trained classifier, the trained classifier trained based at least on a first data item and including both decision determination information of the first data item and decision explanation information of at least one second data item, the second data item being distinct from the first data item; receiving an item for classification; using the trained classifier to classify the item for classification; and providing item decision information regarding a reason for classifying the item for classification, the item decision information being based on at least a part of the decision explanation information. Other embodiments are also described.

    SERVICE USAGE MODEL FOR TRAFFIC ANALYSIS
    8.
    发明申请

    公开(公告)号:US20180212992A1

    公开(公告)日:2018-07-26

    申请号:US15413921

    申请日:2017-01-24

    Abstract: In one embodiment, a device in a network identifies an set of services of a domain accessed by a plurality of users in the network. The device generates a service usage model for the domain based on the set of services accessed by the plurality of users. The service usage model models usage of the services of the domain by the plurality of users. The device trains a machine learning-based classifier to analyze traffic in the network using a set of training feature vectors. A particular training feature vector includes data indicative of service usage by one of the users for the domain and the modeled usage of the services of the domain by the plurality of users. The device causes classification of traffic in the network associated with a particular user by the trained machine learning-based classifier.

    Service usage model for traffic analysis

    公开(公告)号:US10785247B2

    公开(公告)日:2020-09-22

    申请号:US15413921

    申请日:2017-01-24

    Abstract: In one embodiment, a device in a network identifies an set of services of a domain accessed by a plurality of users in the network. The device generates a service usage model for the domain based on the set of services accessed by the plurality of users. The service usage model models usage of the services of the domain by the plurality of users. The device trains a machine learning-based classifier to analyze traffic in the network using a set of training feature vectors. A particular training feature vector includes data indicative of service usage by one of the users for the domain and the modeled usage of the services of the domain by the plurality of users. The device causes classification of traffic in the network associated with a particular user by the trained machine learning-based classifier.

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