Abstract: Described embodiments provide systems and methods for detecting autonomous programs is provided. A device, intermediary to a plurality of clients and a plurality of servers, can receive a first request from a first client of the plurality of clients to a server of the plurality of servers via a connection between the device and the first client. The device can include, into a response from the server to the first client, a uniform resource locator (URL) comprising one or more randomly generated characters within a predetermined character space. The device can determine that the first client has an autonomous program responsive to receiving a second request from the first client using the URL. The device can terminate, responsive to the determination, the connection to the first client.

Abstract: Described embodiments improve the performance of a computer network via selectively forwarding packets to bypass quality of service (QoS) processing, avoiding processing delays during critical periods of high demand, increasing throughput and efficiency may be increased by sacrificing a small amount of QoS accuracy. QoS processing may be applied to a subset of packets of a flow or connection, referred to herein as “lazy” processing or lazy byte batching. Packets that bypass QoS processing may be immediately forwarded with the same QoS settings as packets of the flow for which QoS processing is applied, resulting in tremendous overhead savings with only minimal decline in accuracy.

Abstract: A computer system is provided. The computer system includes a memory, a network interface, and a processor coupled to the memory and the network interface. The processor is configured to receive a response to a request to verify whether an ostensible client of a service is actually a client or a bot, the response including an indicator of whether the ostensible client is a client or a bot; receive information descriptive of interoperations between the ostensible client and the service that are indicative of whether the ostensible client is a client or a bot; and train a plurality of machine learning classifiers using the information and the indicator to generate a next generation of the plurality of machine learning classifiers.

Abstract: Described embodiments improve the performance of a computer network via selectively forwarding packets to bypass quality of service (QoS) processing, avoiding processing delays during critical periods of high demand, increasing throughput and efficiency may be increased by sacrificing a small amount of QoS accuracy. QoS processing may be applied to a subset of packets of a flow or connection, referred to herein as “lazy” processing or lazy byte batching. Packets that bypass QoS processing may be immediately forwarded with the same QoS settings as packets of the flow for which QoS processing is applied, resulting in tremendous overhead savings with only minimal decline in accuracy. In case of backlog, packets may be collected together into an aggregated or ‘uber’ packet, with QoS processing applied based on a virtual size of the aggregated packet.

Abstract: Described embodiments improve the performance of a computer network via selectively forwarding packets to bypass quality of service (QoS) processing, avoiding processing delays during critical periods of high demand, increasing throughput and efficiency may be increased by sacrificing a small amount of QoS accuracy. QoS processing may be applied to a subset of packets of a flow or connection, referred to herein as “lazy” processing or lazy byte batching. Packets that bypass QoS processing may be immediately forwarded with the same QoS settings as packets of the flow for which QoS processing is applied, resulting in tremendous overhead savings with only minimal decline in accuracy. In case of backlog, packets may be collected together into an aggregated or ‘uber’ packet, with QoS processing applied based on a virtual size of the aggregated packet.

Abstract: A computer system is provided. The computer system includes a memory, a network interface, and a processor coupled to the memory and the network interface. The processor is configured to receive a response to a request to verify whether an ostensible client of a service is actually a client or a bot, the response including an indicator of whether the ostensible client is a client or a bot; receive information descriptive of interoperations between the ostensible client and the service that are indicative of whether the ostensible client is a client or a bot; and train a plurality of machine learning classifiers using the information and the indicator to generate a next generation of the plurality of machine learning classifiers.

Abstract: Described embodiments provide systems and methods for detecting autonomous programs is provided. A device, intermediary to a plurality of clients and a plurality of servers, can receive a first request from a first client of the plurality of clients to a server of the plurality of servers via a connection between the device and the first client. The device can include, into a response from the server to the first client, a uniform resource locator (URL) comprising one or more randomly generated characters within a predetermined character space. The device can determine that the first client has an autonomous program responsive to receiving a second request from the first client using the URL. The device can terminate, responsive to the determination, the connection to the first client.

Abstract: Described embodiments improve the performance of a computer network via selectively forwarding packets to bypass quality of service (QoS) processing, avoiding processing delays during critical periods of high demand, increasing throughput and efficiency may be increased by sacrificing a small amount of QoS accuracy. QoS processing may be applied to a subset of packets of a flow or connection, referred to herein as “lazy” processing or lazy byte batching. Packets that bypass QoS processing may be immediately forwarded with the same QoS settings as packets of the flow for which QoS processing is applied, resulting in tremendous overhead savings with only minimal decline in accuracy.

Abstract: Described embodiments improve the performance of a computer network via selectively forwarding packets to bypass quality of service (QoS) processing, avoiding processing delays during critical periods of high demand, increasing throughput and efficiency may be increased by sacrificing a small amount of QoS accuracy. QoS processing may be applied to a subset of packets of a flow or connection, referred to herein as “lazy” processing or lazy byte batching. Packets that bypass QoS processing may be immediately forwarded with the same QoS settings as packets of the flow for which QoS processing is applied, resulting in tremendous overhead savings with only minimal decline in accuracy.

Abstract: Described embodiments improve the performance of a computer network via selectively forwarding packets to bypass quality of service (QoS) processing, avoiding processing delays during critical periods of high demand, increasing throughput and efficiency may be increased by sacrificing a small amount of QoS accuracy. QoS processing may be applied to a subset of packets of a flow or connection, referred to herein as “lazy” processing or lazy byte batching. Packets that bypass QoS processing may be immediately forwarded with the same QoS settings as packets of the flow for which QoS processing is applied, resulting in tremendous overhead savings with only minimal decline in accuracy. In case of backlog, packets may be collected together into an aggregated or ‘uber’ packet, with QoS processing applied based on a virtual size of the aggregated packet.

Abstract: Described embodiments improve the performance of a computer network via selectively forwarding packets to bypass quality of service (QoS) processing, avoiding processing delays during critical periods of high demand, increasing throughput and efficiency may be increased by sacrificing a small amount of QoS accuracy. QoS processing may be applied to a subset of packets of a flow or connection, referred to herein as “lazy” processing or lazy byte batching. Packets that bypass QoS processing may be immediately forwarded with the same QoS settings as packets of the flow for which QoS processing is applied, resulting in tremendous overhead savings with only minimal decline in accuracy.