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.

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.

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.

Abstract: Devices and methods to identify malicious usage of a network device. In at least one embodiment, a network device comprises circuitry for performing a networking function and collecting telemetry data indicative of the performance of the networking function. The network device obtains an inference of a network traffic pattern using a machine learning model, and responds to the inference.

Abstract: Devices and methods to identify malicious usage of a network device. In at least one embodiment, a network device comprises circuitry for performing a networking function and collecting telemetry data indicative of the performance of the networking function. The network device obtains an inference of a network traffic pattern using a machine learning model, and responds to the inference.

Abstract: Methods, systems, and computer program products for selecting packing processing cores are provided. An example system includes a plurality of packet processing cores and a load balancing unit communicatively connected to the plurality of packet processing cores. The load balancing unit is configured to receive a workflow packet including packet description data indicative of at least a packet structure and a packet priority and receive, from the plurality of packet processing cores, state data indicative of at least a utilization state and an operating state of each of the respective packet processing cores. The load balancing unit determines a selected packet processing core from amongst the plurality of packet processing cores based on the state data of the packet processing core and the packet description data of the workflow packet and transmits the workflow packet to the selected packet processing core.

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.

Abstract: In one embodiment, a processing apparatus includes a processor to train an artificial intelligence model to find a pacing action from which to derive a pacing metric for use in serving content transfer requests.

Abstract: In one embodiment, a processing apparatus includes a processor to train an artificial intelligence model as data communication apparatus simulation engine to simulate operation of data communication apparatus, responsively to training data derived from log data collected about the data communication apparatus

Abstract: Devices and methods to identify malicious usage of a network device. In at least one embodiment, a network device comprises circuitry for performing a networking function and collecting telemetry data indicative of the performance of the networking function. The network device obtains an inference of a network traffic pattern using a machine learning model, and responds to the inference.