DETECTION OF MALWARE AND MALICIOUS APPLICATIONS

    公开(公告)号:US20210360004A1

    公开(公告)日:2021-11-18

    申请号:US17360910

    申请日:2021-06-28

    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.

    TRAINING A MACHINE LEARNING-BASED TRAFFIC ANALYZER USING A PROTOTYPE DATASET

    公开(公告)号:US20210357815A1

    公开(公告)日:2021-11-18

    申请号:US17386020

    申请日:2021-07-27

    Abstract: In one embodiment, a device in a network generates a feature vector based on traffic flow data regarding one or more traffic flows in the network. The device makes a determination as to whether the generated feature vector is already represented in a training dataset dictionary by one or more feature vectors in the dictionary. The device updates the training dataset dictionary based on the determination by one of: adding the generated feature vector to the dictionary when the generated feature vector is not already represented by one or more feature vectors in the dictionary, or incrementing a count associated with a particular feature vector in the dictionary when the generated feature vector is already represented by the particular feature vector in the dictionary. The device generates a training dataset based on the training dataset dictionary for training a machine learning-based traffic flow analyzer.

    Leveraging point inferences on HTTP transactions for HTTPS malware detection

    公开(公告)号:US10805341B2

    公开(公告)日:2020-10-13

    申请号:US15889392

    申请日:2018-02-06

    Abstract: In one embodiment, a traffic analysis service receives captured traffic data regarding a Transport Layer Security (TLS) connection between a client and a server. The traffic analysis service applies a first machine learning-based classifier to TLS records from the traffic data, to identify a set of the TLS records that include Hypertext Transfer Protocol (HTTP) header information. The traffic analysis service estimates one or more HTTP transaction labels for the connection by applying a second machine learning-based classifier to the identified set of TLS records that include HTTP header information. The traffic analysis service augments the captured traffic data with the one or more HTTP transaction labels. The traffic analysis service causes performance of a network security function based on the augmented traffic data.

    ENDPOINT-ASSISTED INSPECTION OF ENCRYPTED NETWORK TRAFFIC

    公开(公告)号:US20200053103A1

    公开(公告)日:2020-02-13

    申请号:US16100361

    申请日:2018-08-10

    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.

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