Efficient and flexible flow inspector
    2.
    发明公开

    公开(公告)号:US20230328032A1

    公开(公告)日:2023-10-12

    申请号:US17714207

    申请日:2022-04-06

    CPC classification number: H04L63/0236 H04L63/0263 H04L63/20 H04L69/22

    Abstract: In one embodiment, a data communication device includes a network interface controller to process packets received from at least one of a host device for sending over a network, and at least one remote device over the network, at least one processor to execute computer instructions to receive a configuration, and extract filtering rules from the configuration, and at least one hardware accelerator to receive the filtering rules from the at least one processor, and filter the packets based on the rules so that some of the packets are dropped and some of the packets are forwarded to the at least one processor to send data based on the forwarded packets to another device.

    DISTRIBUTED DENIAL OF SERVICE (DDOS) BASED ARTIFICIAL INTELLIGENCE (AI) ACCELERATED SOLUTION USING A DATA PROCESSING UNIT (DPU)

    公开(公告)号:US20250097260A1

    公开(公告)日:2025-03-20

    申请号:US18369710

    申请日:2023-09-18

    Abstract: Apparatuses, systems, and techniques for detecting that a host device is subject to a distributed denial of service (DDOS) attack using a machine learning (ML) detection system are described. A computing system includes a data processing unit (DPU) with a network interface and a hardware acceleration engine. The DPU hosts a hardware-accelerated security service to extract features from network data and metadata from the hardware acceleration engine and sends the extracted features to the ML detection system. The ML detection system determines whether the host device is subject to a DDOS attack using the extracted features. The ML detection system can send an enforcement rule to the hardware acceleration engine responsive to a determination that the host device is subject to the DDOS attack.

    Efficient and flexible flow inspector

    公开(公告)号:US12231401B2

    公开(公告)日:2025-02-18

    申请号:US17714207

    申请日:2022-04-06

    Abstract: In one embodiment, a data communication device includes a network interface controller to process packets received from at least one of a host device for sending over a network, and at least one remote device over the network, at least one processor to execute computer instructions to receive a configuration, and extract filtering rules from the configuration, and at least one hardware accelerator to receive the filtering rules from the at least one processor, and filter the packets based on the rules so that some of the packets are dropped and some of the packets are forwarded to the at least one processor to send data based on the forwarded packets to another device.

    DISTRIBUTED DENIAL OF SERVICE (DDOS) BASED ARTIFICIAL INTELLIGENCE (AI) ACCELERATED SOLUTION USING A SWITCH

    公开(公告)号:US20250097261A1

    公开(公告)日:2025-03-20

    申请号:US18369714

    申请日:2023-09-18

    Abstract: Apparatuses, systems, and techniques for detecting that a host device is subject to a distributed denial of service (DDOS) attack using a machine learning (ML) detection system are described. A computing system includes a switch with port interfaces, a central processing unit (CPU) that implements a machine learning (ML) detection system, and network monitoring logic. The network monitoring logic can extract features from network data and send the extracted features to the ML detection system. The ML detection system determines whether the host device is subject to a DDOS attack using the extracted features. The ML detection system can send an alert to the host device responsive to a determination that the host device is subject to the DDOS attack.

    ACCELERATED DATA MOVEMENT BETWEEN DATA PROCESSING UNIT (DPU) AND GRAPHICS PRCESSING UNIT (GPU) TO ADDRESS REAL-TIME CYBERSECURITY REQURIEMENTS

    公开(公告)号:US20240396916A1

    公开(公告)日:2024-11-28

    申请号:US18788700

    申请日:2024-07-30

    Abstract: Apparatuses, systems, and techniques for detecting that a host device is subject to a malicious network attack using a machine learning (ML) detection system are described. A computing system includes a graphics processing unit (GPU) and an integrated circuit with a network interface, and a hardware acceleration engine. The integrated circuit hosts a hardware-accelerated security service to extract features from network data and metadata from the hardware acceleration engine and sends the extracted features to the GPU. Using the ML detection system, the GPU determines whether the host device is subject to a malicious network attack using the extracted features. The GPU can send an enforcement rule to the integrated circuit responsive to a determination that the host device is subject to the malicious network activity.

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