PRESERVING PRIVACY IN EXPORTING DEVICE CLASSIFICATION RULES FROM ON-PREMISE SYSTEMS

    公开(公告)号:US20200382553A1

    公开(公告)日:2020-12-03

    申请号:US16424912

    申请日:2019-05-29

    Abstract: In one embodiment, a device in a network obtains data indicative of a device classification rule, a device type label associated with the rule, and a set of positive and negative feature vectors used to create the rule. The device replaces similar feature vectors in the set of positive and negative feature vectors with a single feature vector, to form a reduced set of feature vectors. The device applies differential privacy to the reduced set of feature vectors. The device sends a digest to a cloud service. The digest comprises the device classification rule, the device type label, and the reduced set of feature vectors to which differential privacy was applied. The service uses the digest to train a machine learning-based device classifier.

    DEEP LEARNING ARCHITECTURE FOR COLLABORATIVE ANOMALY DETECTION AND EXPLANATION

    公开(公告)号:US20200076677A1

    公开(公告)日:2020-03-05

    申请号:US16120529

    申请日:2018-09-04

    Abstract: In one embodiment, a network assurance service that monitors a network detects a behavioral anomaly in the network using an anomaly detector that compares an anomaly detection threshold to a target value calculated based on a first set of one or more measurements from the network. The service uses an explanation model to predict when the anomaly detector will detect anomalies. The explanation model takes as input a second set of one or more measurements from the network that differs from the first set. The service determines that the detected anomaly is explainable, based on the explanation model correctly predicting the detection of the anomaly by the anomaly detector. The service provides an anomaly detection alert for the detected anomaly to a user interface, based on the detected anomaly being explainable. The anomaly detection alert indicates at least one measurement from the second set as an explanation for the anomaly.

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