Budgeted neural network architecture search system and method

    公开(公告)号:US12050979B2

    公开(公告)日:2024-07-30

    申请号:US16357603

    申请日:2019-03-19

    CPC classification number: G06N3/044 G06N3/08

    Abstract: A neural network architecture search may be conducted by a controller to generate a neural network. The controller may perform the search by generating a directed acyclic graph across nodes in a search space, the nodes representing compute operations for a neural network. As the search is performed, the controller may retrieve resource availability information to modify the likelihood of a generated neural network architecture including previously unused nodes.

    Federated microburst detection
    2.
    发明授权

    公开(公告)号:US10972388B2

    公开(公告)日:2021-04-06

    申请号:US15359511

    申请日:2016-11-22

    Abstract: An example method includes a sensor detecting multiple packets of a flow during a specified total time period (e.g., a reporting time period). The total time period can be subdivided into multiple time periods. The sensor can analyze the detected packets to determine an amount of network utilization for each of the time periods. The sensor can then generate a flow summary based on the network utilization and the flow and send the flow summary to an analytics engine. Multiple other sensors can do similarly for their respective packets and flows. The analytics engine can receive the flow summaries from the various sensors and determine a correspondence between flow with high network utilization at a specific time period and a node or nodes. These nodes that experienced multiple flows with high network utilization for a certain period of time can be identified as experiencing a microburst.

    TECHNIQUES FOR DETECTING AND MITIGATING SPOOFED EMAIL COMMUNICATIONS

    公开(公告)号:US20250106246A1

    公开(公告)日:2025-03-27

    申请号:US18474555

    申请日:2023-09-26

    Abstract: Techniques are described herein for detecting an invalid (e.g., spoof) email before it is received by an intended recipient. In some embodiments, the techniques may involve, upon receiving an electronic communication directed to an intended recipient, determining, based on information included in the electronic communication, a claimed source entity, and determining a domain associated with the email communication. The techniques may further involve determining an owner entity associated with the domain and then determining that the electronic communication is valid based on a comparison between the owner entity and the claimed source entity. Upon determining that the electronic communication is not valid, the techniques may further comprise performing one or more mitigation techniques.

    Neural architecture construction using envelopenets for image recognition

    公开(公告)号:US10902293B2

    公开(公告)日:2021-01-26

    申请号:US16177581

    申请日:2018-11-01

    Abstract: In one embodiment, a device forms a neural network envelope cell that comprises a plurality of convolution-based filters in series or parallel. The device constructs a convolutional neural network by stacking copies of the envelope cell in series. The device trains, using a training dataset of images, the convolutional neural network to perform image classification by iteratively collecting variance metrics for each filter in each envelope cell, pruning filters with low variance metrics from the convolutional neural network, and appending a new copy of the envelope cell into the convolutional neural network.

    SYSTEMS AND METHODS FOR AUTO MACHINE LEARNING AND NEURAL ARCHITECTURE SEARCH

    公开(公告)号:US20200302272A1

    公开(公告)日:2020-09-24

    申请号:US16358554

    申请日:2019-03-19

    Abstract: The present disclosure provides systems, methods and computer-readable media for optimizing the neural architecture search for the automated machine learning process. In one aspect, neural architecture search method including selecting a neural architecture for training as part of an automated machine learning process; collecting statistical parameters on individual nodes of the neural architecture during the training; determining, based on the statistical parameters, active nodes of the neural architecture to form a candidate neural architecture; and validating the candidate neural architecture to produce a trained neural architecture to be used in implemented an application or a service.

    Detecting and mitigating multi-stage email threats

    公开(公告)号:US12238054B2

    公开(公告)日:2025-02-25

    申请号:US17699579

    申请日:2022-03-21

    Abstract: Techniques for an email-security system to detect multi-stage email scam attacks, and engage an attacker to obtain additional information. The system may analyze emails for users and identify scam emails by analyzing metadata of the emails. The system may then classify the scam emails into particular classes from among a group of scam-email classes. The system may then engage the attacker that sent the scam email. In some instances, the scam emails may be multi-stage attacks, and the system may automatically engage the attacker to move to the next stage of the scam attack. For instance, the system may send a lure email that is responsive to the particular scam class to prompt or provoke the attacker to send more sensitive information, such as a phone number, a bank account, etc. The system may then harvest this sensitive information of the attacker, and use that information for various remedial actions.

    DETECTING AND MITIGATING MULTI-STAGE EMAIL THREATS

    公开(公告)号:US20230171213A1

    公开(公告)日:2023-06-01

    申请号:US17699579

    申请日:2022-03-21

    CPC classification number: H04L51/12 H04L51/22 H04L63/1433 G06N20/00

    Abstract: Techniques for an email-security system to detect multi-stage email scam attacks, and engage an attacker to obtain additional information. The system may analyze emails for users and identify scam emails by analyzing metadata of the emails. The system may then classify the scam emails into particular classes from among a group of scam-email classes. The system may then engage the attacker that sent the scam email. In some instances, the scam emails may be multi-stage attacks, and the system may automatically engage the attacker to move to the next stage of the scam attack. For instance, the system may send a lure email that is responsive to the particular scam class to prompt or provoke the attacker to send more sensitive information, such as a phone number, a bank account, etc. The system may then harvest this sensitive information of the attacker, and use that information for various remedial actions.

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