SERVICE USAGE MODEL FOR TRAFFIC ANALYSIS
    21.
    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.

    Detection of malicious network connections

    公开(公告)号:US09531742B2

    公开(公告)日:2016-12-27

    申请号:US15095076

    申请日:2016-04-10

    Abstract: In one embodiment a method, system and apparatus is described for detecting a malicious network connection, the method system and apparatus including determining, for each connection over a network, if each connection is a persistent connection, if, as a result of the determining, a first connection is determined to be a persistent connection, collecting connection statistics for the first connection, creating a feature vector for the first connection based on the collected statistics, performing outlier detection for all of the feature vector for all connections over a network which have been determined to be persistent connections, and reporting detected outliers. Related methods, systems and apparatus are also described.

    Detection of malicious executable files using hierarchical models

    公开(公告)号:US11113397B2

    公开(公告)日:2021-09-07

    申请号:US16413880

    申请日:2019-05-16

    Abstract: In one embodiment, a device disassembles an executable file into assembly instructions. The device maps each of the assembly instructions to a fixed length instruction vector using one-hot encoding and an instruction vocabulary and forms vector representations of blocks of a control flow graph for corresponding functions of the executable file by embedding and aggregating bags of the instruction vectors. The device generates, based on the vector representations of the blocks of the control flow graph, a call graph model of the functions in the executable file. The device forms a vector representation of the executable file based in part on the call graph model. The device determines, based on the vector representation of the executable file, whether the executable file is malware.

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